{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b5c96e62",
   "metadata": {},
   "source": [
    "# Lesson 19: Regression models\n",
    "\n",
    "This notebook uses linear regression as a baseline, then compares several other regression models on the concrete dataset. The final section shows Poisson regression on count data.\n",
    "\n",
    "1. **Linear regression**\n",
    "2. **K-nearest neighbors (KNN) regression**\n",
    "3. **Decision tree regression**\n",
    "4. **Random forest regression**\n",
    "5. **Optimized random forest regression**\n",
    "6. **Kernel idea (SVR and GPR intuition)**\n",
    "7. **Support vector regression (SVR)**\n",
    "8. **Gaussian process regression (GPR)**\n",
    "9. **Model comparison**\n",
    "10. **Poisson regression**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9314037",
   "metadata": {},
   "source": [
    "## Notebook setup\n",
    "\n",
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4e1397a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import root_mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6726c10e",
   "metadata": {},
   "source": [
    "### Dataset: concrete compressive strength\n",
    "\n",
    "The [UCI concrete compressive strength dataset](https://archive.ics.uci.edu/dataset/165/concrete+compressive+strength) has 1,030 mix designs with ingredient amounts, curing age, and measured strength in MPa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "464ba14c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (1030, 9)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cement</th>\n",
       "      <th>slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_agg</th>\n",
       "      <th>fine_agg</th>\n",
       "      <th>age</th>\n",
       "      <th>strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>79.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>40.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>41.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.6</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.4</td>\n",
       "      <td>825.5</td>\n",
       "      <td>360.0</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cement   slag  fly_ash  water  superplasticizer  coarse_agg  fine_agg  \\\n",
       "0   540.0    0.0      0.0  162.0               2.5      1040.0     676.0   \n",
       "1   540.0    0.0      0.0  162.0               2.5      1055.0     676.0   \n",
       "2   332.5  142.5      0.0  228.0               0.0       932.0     594.0   \n",
       "3   332.5  142.5      0.0  228.0               0.0       932.0     594.0   \n",
       "4   198.6  132.4      0.0  192.0               0.0       978.4     825.5   \n",
       "\n",
       "     age  strength  \n",
       "0   28.0     79.99  \n",
       "1   28.0     61.89  \n",
       "2  270.0     40.27  \n",
       "3  365.0     41.05  \n",
       "4  360.0     44.30  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load from OpenML (cached after first download)\n",
    "raw = fetch_openml(data_id=4353, as_frame=True, parser='auto')\n",
    "df  = raw.frame.copy()\n",
    "\n",
    "df.columns = [\n",
    "    'cement', 'slag', 'fly_ash', 'water', 'superplasticizer',\n",
    "    'coarse_agg', 'fine_agg', 'age', 'strength'\n",
    "]\n",
    "\n",
    "print(f'Shape: {df.shape}')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57692151",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero counts (ingredients not present in every mix):\n",
      "slag                471\n",
      "fly_ash             566\n",
      "superplasticizer    379\n",
      "\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cement</th>\n",
       "      <th>slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_agg</th>\n",
       "      <th>fine_agg</th>\n",
       "      <th>age</th>\n",
       "      <th>strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1030.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>1030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>281.2</td>\n",
       "      <td>73.9</td>\n",
       "      <td>54.2</td>\n",
       "      <td>181.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>972.9</td>\n",
       "      <td>773.6</td>\n",
       "      <td>45.7</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.5</td>\n",
       "      <td>86.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>77.8</td>\n",
       "      <td>80.2</td>\n",
       "      <td>63.2</td>\n",
       "      <td>16.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>102.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>192.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>164.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>272.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>968.0</td>\n",
       "      <td>779.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>350.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>118.3</td>\n",
       "      <td>192.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>1029.4</td>\n",
       "      <td>824.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>46.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>540.0</td>\n",
       "      <td>359.4</td>\n",
       "      <td>200.1</td>\n",
       "      <td>247.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>992.6</td>\n",
       "      <td>365.0</td>\n",
       "      <td>82.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cement    slag  fly_ash   water  superplasticizer  coarse_agg  \\\n",
       "count  1030.0  1030.0   1030.0  1030.0            1030.0      1030.0   \n",
       "mean    281.2    73.9     54.2   181.6               6.2       972.9   \n",
       "std     104.5    86.3     64.0    21.4               6.0        77.8   \n",
       "min     102.0     0.0      0.0   121.8               0.0       801.0   \n",
       "25%     192.4     0.0      0.0   164.9               0.0       932.0   \n",
       "50%     272.9    22.0      0.0   185.0               6.4       968.0   \n",
       "75%     350.0   143.0    118.3   192.0              10.2      1029.4   \n",
       "max     540.0   359.4    200.1   247.0              32.2      1145.0   \n",
       "\n",
       "       fine_agg     age  strength  \n",
       "count    1030.0  1030.0    1030.0  \n",
       "mean      773.6    45.7      35.8  \n",
       "std        80.2    63.2      16.7  \n",
       "min       594.0     1.0       2.3  \n",
       "25%       731.0     7.0      23.7  \n",
       "50%       779.5    28.0      34.4  \n",
       "75%       824.0    56.0      46.1  \n",
       "max       992.6   365.0      82.6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Many mixes don't use every ingredient\n",
    "zero_counts = (df[['slag', 'fly_ash', 'superplasticizer']] == 0).sum()\n",
    "print('Zero counts (ingredients not present in every mix):')\n",
    "print(zero_counts.to_string())\n",
    "print()\n",
    "df.describe().round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f221ff9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 3))\n",
    "\n",
    "axes[0].hist(df['strength'], bins=30, color='steelblue', edgecolor='white')\n",
    "axes[0].set_xlabel('Compressive strength (MPa)')\n",
    "axes[0].set_title('Target distribution')\n",
    "\n",
    "axes[1].scatter(df['age'], df['strength'], s=8, alpha=0.3, color='teal')\n",
    "axes[1].set_xlabel('Curing age (days)')\n",
    "axes[1].set_ylabel('Strength (MPa)')\n",
    "axes[1].set_title('Strength vs curing age')\n",
    "\n",
    "axes[2].scatter(df['cement'], df['strength'], s=8, alpha=0.3, color='purple')\n",
    "axes[2].set_xlabel('Cement content (kg/m³)')\n",
    "axes[2].set_ylabel('Strength (MPa)')\n",
    "axes[2].set_title('Strength vs cement content')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db29c4a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features (8): ['cement', 'slag', 'fly_ash', 'water', 'superplasticizer', 'coarse_agg', 'fine_agg', 'age']\n",
      "Target range: 2.3 to 82.6 MPa\n",
      "Mean strength: 35.8 MPa\n",
      "\n",
      "Training samples: 824\n",
      "Testing samples:  206\n"
     ]
    }
   ],
   "source": [
    "feature_names = [c for c in df.columns if c != 'strength']\n",
    "X = df[feature_names].values\n",
    "y = df['strength'].values\n",
    "\n",
    "print(f'Features ({len(feature_names)}): {feature_names}')\n",
    "print(f'Target range: {y.min():.1f} to {y.max():.1f} MPa')\n",
    "print(f'Mean strength: {y.mean():.1f} MPa')\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=315)\n",
    "print(f'\\nTraining samples: {X_train.shape[0]}')\n",
    "print(f'Testing samples:  {X_test.shape[0]}')\n",
    "\n",
    "results = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c385dd47",
   "metadata": {},
   "source": [
    "## 1. Linear regression\n",
    "\n",
    "Linear regression fits a weighted sum of the inputs plus an intercept.\n",
    "\n",
    "### Prediction equation\n",
    "\n",
    "$$\n",
    "\\hat{y} = \\mathbf{w}^\\top \\mathbf{x} + b\n",
    "$$\n",
    "\n",
    "### What is learned during training\n",
    "\n",
    "- One coefficient for each feature in $\\mathbf{w}$\n",
    "- One intercept value $b$\n",
    "\n",
    "### How to calculate one prediction\n",
    "\n",
    "1. Take the feature row $\\mathbf{x}$ for the new sample.\n",
    "2. Multiply each feature by its learned coefficient and sum.\n",
    "3. Add the learned intercept $b$.\n",
    "4. Return the result as the prediction.\n",
    "\n",
    "Use it as a baseline when you want a fast, interpretable first pass.\n",
    "\n",
    "**Strengths:**\n",
    "- Fast\n",
    "- Easy to interpret\n",
    "- Good baseline\n",
    "\n",
    "**Weaknesses:**\n",
    "- Limited for non-linear patterns\n",
    "- Sensitive to outliers\n",
    "- Weak on interactions without feature engineering\n",
    "\n",
    "**Important hyperparameters:**\n",
    "- `fit_intercept`: include an intercept term\n",
    "- `copy_X`: keep a copy of the training matrix\n",
    "- `n_jobs`: parallelization for some solver paths\n",
    "\n",
    "**Preprocessing:** not required for fitting, though scaling can help if you want to compare coefficient sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ee017da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression RMSE: 10.97 MPa\n",
      "Linear regression R²:   0.596\n"
     ]
    }
   ],
   "source": [
    "lin_model = LinearRegression()\n",
    "lin_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_lin = lin_model.predict(X_test)\n",
    "lin_rmse   = root_mean_squared_error(y_test, y_pred_lin)\n",
    "lin_r2     = r2_score(y_test, y_pred_lin)\n",
    "\n",
    "print(f'Linear regression RMSE: {lin_rmse:.2f} MPa')\n",
    "print(f'Linear regression R²:   {lin_r2:.3f}')\n",
    "results['Linear regression'] = {'rmse': lin_rmse, 'r2': lin_r2}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91930cf",
   "metadata": {},
   "source": [
    "### Side note: scaling\n",
    "\n",
    "#### Scaling features\n",
    "\n",
    "Does not effect prediction performance, but changes parameter estimates (**think** why?)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e2301bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression (scaled features) RMSE: 10.97 MPa\n",
      "Linear regression (scaled features)  R²:   0.596\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "feature_scaler = StandardScaler()\n",
    "feature_scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = feature_scaler.transform(X_train)\n",
    "X_test_scaled = feature_scaler.transform(X_test)\n",
    "\n",
    "feature_scaled_model = LinearRegression()\n",
    "feature_scaled_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred_feature_scaled = feature_scaled_model.predict(X_test_scaled)\n",
    "feature_scaled_rmse   = root_mean_squared_error(y_test, y_pred_feature_scaled)\n",
    "feature_scaled_r2     = r2_score(y_test, y_pred_feature_scaled)\n",
    "\n",
    "print(f'Linear regression (scaled features) RMSE: {feature_scaled_rmse:.2f} MPa')\n",
    "print(f'Linear regression (scaled features)  R²:   {feature_scaled_r2:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "172d060c",
   "metadata": {},
   "source": [
    "#### Scaling labels\n",
    "\n",
    "Scaling the label will effect RSS/RMSE/MAE metrics, but not R-squared (**think** why?)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7baaaa05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression (scaled label) RMSE: 0.66 MPa\n",
      "Linear regression (scaled label) R²:   0.596\n"
     ]
    }
   ],
   "source": [
    "label_scaler = StandardScaler()\n",
    "label_scaler.fit(y_train.reshape(-1, 1))\n",
    "\n",
    "y_train_scaled = label_scaler.transform(y_train.reshape(-1, 1))\n",
    "y_test_scaled = label_scaler.transform(y_test.reshape(-1, 1))\n",
    "\n",
    "label_scaled_model = LinearRegression()\n",
    "label_scaled_model.fit(X_train, y_train_scaled)\n",
    "\n",
    "y_pred_lin = label_scaled_model.predict(X_test)\n",
    "lin_rmse   = root_mean_squared_error(y_test_scaled, y_pred_lin)\n",
    "lin_r2     = r2_score(y_test_scaled, y_pred_lin)\n",
    "\n",
    "print(f'Linear regression (scaled label) RMSE: {lin_rmse:.2f} MPa')\n",
    "print(f'Linear regression (scaled label) R²:   {lin_r2:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9ccd30",
   "metadata": {},
   "source": [
    "To get predictions in the original units, use the fitted scaler's `inverse_transform()` method on the raw output from the model to \"undo\" the scaling. Many Scikit-Learn transformers have this method for exactly this reason. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86bb1650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression (unscaled predictions) RMSE: 10.97 MPa\n",
      "Linear regression (unscaled predictions) R²:   0.596\n"
     ]
    }
   ],
   "source": [
    "unscaled_y_pred_lin = label_scaler.inverse_transform(y_pred_lin)\n",
    "lin_rmse   = root_mean_squared_error(y_test, unscaled_y_pred_lin)\n",
    "lin_r2     = r2_score(y_test, unscaled_y_pred_lin)\n",
    "\n",
    "print(f'Linear regression (unscaled predictions) RMSE: {lin_rmse:.2f} MPa')\n",
    "print(f'Linear regression (unscaled predictions) R²:   {lin_r2:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd78e820",
   "metadata": {},
   "source": [
    "#### Don't \"mix-and-match\"!\n",
    "\n",
    "Can you spot the problem in the following code?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55c5858c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression (bad) RMSE: 7187.55 MPa\n",
      "Linear regression (bad) R²:   -173479.223\n"
     ]
    }
   ],
   "source": [
    "bad_predictions = feature_scaled_model.predict(X_test)\n",
    "\n",
    "lin_rmse = root_mean_squared_error(y_test, bad_predictions)\n",
    "lin_r2   = r2_score(y_test, bad_predictions)\n",
    "\n",
    "print(f'Linear regression (bad) RMSE: {lin_rmse:.2f} MPa')\n",
    "print(f'Linear regression (bad) R²:   {lin_r2:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf4b2af",
   "metadata": {},
   "source": [
    "The large RMSE value relative to the other experiments, and especially the huge negative R-squared should tell you that there is something fundamentally wrong here; way beyond just low model performance...\n",
    "\n",
    "## 2. K-nearest neighbors (KNN) regression\n",
    "\n",
    "KNN predicts from the average target value of the nearest training points.\n",
    "\n",
    "### Prediction equation\n",
    "\n",
    "$$\n",
    "\\hat{y}(x)=\\frac{\\sum_{i \\in \\mathcal{N}_k(x)} w_i\\,y_i}{\\sum_{i \\in \\mathcal{N}_k(x)} w_i}\n",
    "$$\n",
    "\n",
    "### What is learned during training\n",
    "\n",
    "- KNN is mostly instance-based: it stores the training set\n",
    "- It uses the chosen distance metric and neighbor settings\n",
    "- With distance weighting, it uses larger weights for closer neighbors\n",
    "\n",
    "### How to calculate one prediction\n",
    "\n",
    "1. Scale the new sample using the fitted scaler.\n",
    "2. Compute distances from the new sample to all training samples.\n",
    "3. Select the $k$ closest neighbors.\n",
    "4. Compute neighbor weights from distance (or uniform weights).\n",
    "5. Return the weighted average of neighbor target values.\n",
    "\n",
    "**Strengths:**\n",
    "- Simple\n",
    "- Local and flexible\n",
    "- Strong non-parametric baseline\n",
    "\n",
    "**Weaknesses:**\n",
    "- Slow at prediction time\n",
    "- Sensitive to scale\n",
    "- Struggles in high dimensions\n",
    "\n",
    "**Important hyperparameters:**\n",
    "- `n_neighbors`: number of neighbors to average\n",
    "- `weights`: use uniform or distance weighting\n",
    "- `metric`: distance function used for neighbor search\n",
    "\n",
    "**Preprocessing:** scaling is essential."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e915bb65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN RMSE: 7.84 MPa\n",
      "KNN R²:   0.794\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "knn_model = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('knn',    KNeighborsRegressor(n_neighbors=5, weights='distance'))\n",
    "])\n",
    "knn_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_knn = knn_model.predict(X_test)\n",
    "knn_rmse   = root_mean_squared_error(y_test, y_pred_knn)\n",
    "knn_r2     = r2_score(y_test, y_pred_knn)\n",
    "\n",
    "print(f'KNN RMSE: {knn_rmse:.2f} MPa')\n",
    "print(f'KNN R²:   {knn_r2:.3f}')\n",
    "results['KNN'] = {'rmse': knn_rmse, 'r2': knn_r2}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902ae8c6",
   "metadata": {},
   "source": [
    "## 3. Decision tree regression\n",
    "\n",
    "A decision tree splits the feature space into regions and predicts the mean target value in each leaf.\n",
    "\n",
    "### Prediction equation\n",
    "\n",
    "$$\n",
    "\\hat{y}(x)=c_{m(x)}\n",
    "$$\n",
    "\n",
    "Here, $m(x)$ is the leaf reached by $x$, and $c_{m(x)}$ is that leaf's learned value.\n",
    "\n",
    "### What is learned during training\n",
    "\n",
    "- Which feature to split on at each node\n",
    "- The threshold value for each split\n",
    "- The leaf prediction value (typically mean target in that leaf)\n",
    "\n",
    "### How to calculate one prediction\n",
    "\n",
    "1. Start at the root node.\n",
    "2. Compare the relevant feature to the node threshold.\n",
    "3. Move left or right based on the comparison.\n",
    "4. Repeat until reaching a leaf.\n",
    "5. Return that leaf's prediction value.\n",
    "\n",
    "**Strengths:**\n",
    "- No scaling needed\n",
    "- Captures non-linear patterns\n",
    "- Easy to visualize\n",
    "\n",
    "**Weaknesses:**\n",
    "- Can overfit\n",
    "- Produces step-like predictions\n",
    "- Poor extrapolation\n",
    "\n",
    "**Important hyperparameters:**\n",
    "- `max_depth`: limit tree depth\n",
    "- `min_samples_leaf`: keep leaves from getting too small\n",
    "- `min_samples_split`: require enough samples before splitting\n",
    "- `max_features`: limit features considered at each split\n",
    "\n",
    "**Preprocessing:** none required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1fc37f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree RMSE: 8.61 MPa\n",
      "Decision tree R²:   0.751\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "dt_model = DecisionTreeRegressor(max_depth=5, random_state=315)\n",
    "result = dt_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_test_dt = dt_model.predict(X_test)\n",
    "dt_test_rmse   = root_mean_squared_error(y_test, y_pred_test_dt)\n",
    "dt_test_r2     = r2_score(y_test, y_pred_test_dt)\n",
    "\n",
    "print(f'Decision tree RMSE: {dt_test_rmse:.2f} MPa')\n",
    "print(f'Decision tree R²:   {dt_test_r2:.3f}')\n",
    "results['Decision tree'] = {'rmse': dt_test_rmse, 'r2': dt_test_r2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9b83a0c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plot_tree(dt_model, max_depth=2, feature_names=feature_names, fontsize=10, filled=True, rounded=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1e0875",
   "metadata": {},
   "source": [
    "## 4. Random forest regression\n",
    "\n",
    "A random forest averages many decision trees to reduce variance and improve stability.\n",
    "\n",
    "### Prediction equation\n",
    "\n",
    "$$\n",
    "\\hat{y}(x)=\\frac{1}{T}\\sum_{t=1}^{T} f_t(x)\n",
    "$$\n",
    "\n",
    "where $f_t(x)$ is the prediction from tree $t$, and $T$ is the number of trees.\n",
    "\n",
    "### What is learned during training\n",
    "\n",
    "- The full structure of each tree: splits, thresholds, and leaf values\n",
    "- The ensemble average behavior across all trained trees\n",
    "\n",
    "### How to calculate one prediction\n",
    "\n",
    "1. Send the new sample through each tree.\n",
    "2. Get one prediction from each tree.\n",
    "3. Average all tree predictions.\n",
    "4. Return that average as the forest prediction.\n",
    "\n",
    "**Strengths:**\n",
    "- Usually stronger than a single tree\n",
    "- Handles non-linear patterns well\n",
    "- Gives feature importance estimates\n",
    "\n",
    "**Weaknesses:**\n",
    "- Less interpretable\n",
    "- Uses more memory\n",
    "- Still cannot extrapolate well\n",
    "\n",
    "**Important hyperparameters:**\n",
    "- `n_estimators`: number of trees\n",
    "- `max_depth`: maximum depth of each tree\n",
    "- `min_samples_leaf`: minimum samples per leaf\n",
    "- `max_features`: number of features checked at each split\n",
    "\n",
    "**Preprocessing:** none required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61be1127",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest  RMSE: 5.80 MPa\n",
      "Random forest  R²:   0.887\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf_model = RandomForestRegressor(\n",
    "    n_estimators=200,\n",
    "    max_depth=10,\n",
    "    min_samples_leaf=3,\n",
    "    random_state=315,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = rf_model.predict(X_test)\n",
    "rf_rmse   = root_mean_squared_error(y_test, y_pred_rf)\n",
    "rf_r2     = r2_score(y_test, y_pred_rf)\n",
    "\n",
    "print(f'Random forest  RMSE: {rf_rmse:.2f} MPa')\n",
    "print(f'Random forest  R²:   {rf_r2:.3f}')\n",
    "results['Random forest'] = {'rmse': rf_rmse, 'r2': rf_r2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00c917f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = pd.Series(rf_model.feature_importances_, index=feature_names).sort_values()\n",
    "importances.plot(kind='barh', color='steelblue', title='Feature importance')\n",
    "plt.xlabel('Importance')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b12f6ca3",
   "metadata": {},
   "source": [
    "## 5. Optimized random forest regression\n",
    "\n",
    "Here we tune a random forest with cross-validation and keep the best setting. A small search for optimal settings can often beat a hand-picked defaults with little extra complexity.\n",
    "\n",
    "**Important hyperparameters:**\n",
    "- `n_estimators`: number of trees to search over\n",
    "- `max_depth`: tree depth values to test\n",
    "- `min_samples_leaf`: leaf size values to test\n",
    "- `cv`: number of cross-validation folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d14b041",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'max_depth': 18, 'min_samples_leaf': 1, 'n_estimators': 600}\n",
      "Optimized random forest RMSE: 5.18 MPa\n",
      "Optimized random forest R²:   0.910\n",
      "\n",
      "CPU times: user 8.73 s, sys: 336 ms, total: 9.07 s\n",
      "Wall time: 1min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "rf_param_grid = {\n",
    "    'n_estimators': [200, 400, 500, 600],\n",
    "    'max_depth': [4, 6, 8, 10, 12, 14, 16, 18],\n",
    "    'min_samples_leaf': [1, 2, 3, 4]\n",
    "}\n",
    "\n",
    "rf_search = GridSearchCV(\n",
    "    RandomForestRegressor(random_state=315, n_jobs=-1),\n",
    "    rf_param_grid,\n",
    "    scoring='neg_root_mean_squared_error',\n",
    "    cv=5,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "rf_search.fit(X_train, y_train)\n",
    "\n",
    "opt_rf_model = rf_search.best_estimator_\n",
    "y_pred_opt_rf = opt_rf_model.predict(X_test)\n",
    "opt_rf_rmse = root_mean_squared_error(y_test, y_pred_opt_rf)\n",
    "opt_rf_r2 = r2_score(y_test, y_pred_opt_rf)\n",
    "\n",
    "print(f'Best parameters: {rf_search.best_params_}')\n",
    "print(f'Optimized random forest RMSE: {opt_rf_rmse:.2f} MPa')\n",
    "print(f'Optimized random forest R²:   {opt_rf_r2:.3f}')\n",
    "results['Optimized random forest'] = {'rmse': opt_rf_rmse, 'r2': opt_rf_r2}\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3e20ae",
   "metadata": {},
   "source": [
    "## 6. Kernel idea (SVR and GPR intuition)\n",
    "\n",
    "A kernel is a way to measure how similar two input rows are. Think of this as a non-linear distance score:\n",
    "- If two rows are very similar (close together in feature space), the kernel value is large\n",
    "- If they are far different (far apart in feature space), the kernel value is small\n",
    "- The relationship between distance and kernel value is not linear\n",
    "\n",
    "In SVR and GPR, the kernel is what lets the model capture curved patterns without us manually creating engineered features.\n",
    "\n",
    "A common choice is the RBF kernel:\n",
    "$$\n",
    "K(x_i, x_j) = \\exp(-\\gamma\\,\\|x_i-x_j\\|^2)\n",
    "$$\n",
    "\n",
    "You do not need linear algebra here. Just remember:\n",
    "- kernel value near 1 means very similar\n",
    "- kernel value near 0 means not similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87e70d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual intuition for an RBF kernel in 1D\n",
    "x = np.linspace(-3, 3, 120)\n",
    "anchor = 0.5\n",
    "gamma = 1.2\n",
    "\n",
    "similarity_to_anchor = np.exp(-gamma * (x - anchor) ** 2)\n",
    "\n",
    "# Pairwise similarity matrix: bright cells are highly similar pairs\n",
    "X1, X2 = np.meshgrid(x, x)\n",
    "K = np.exp(-gamma * (X1 - X2) ** 2)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n",
    "\n",
    "axes[0].plot(x, similarity_to_anchor, color='teal', linewidth=2)\n",
    "axes[0].axvline(anchor, color='black', linestyle='--', linewidth=1)\n",
    "axes[0].set_title('Similarity to one anchor point')\n",
    "axes[0].set_xlabel('x value')\n",
    "axes[0].set_ylabel('RBF kernel similarity')\n",
    "axes[0].set_ylim(-0.02, 1.05)\n",
    "\n",
    "im = axes[1].imshow(\n",
    "    K,\n",
    "    origin='lower',\n",
    "    extent=[x.min(), x.max(), x.min(), x.max()],\n",
    "    aspect='auto',\n",
    "    cmap='viridis'\n",
    ")\n",
    "\n",
    "axes[1].set_title('Kernel similarity matrix')\n",
    "axes[1].set_xlabel('x_j')\n",
    "axes[1].set_ylabel('x_i')\n",
    "fig.colorbar(im, ax=axes[1], label='similarity')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ccf7020",
   "metadata": {},
   "source": [
    "## 7. Support vector regression (SVR)\n",
    "\n",
    "SVR predicts with a smooth curve while ignoring small errors inside an `epsilon` tube. A simple way to think about it:\n",
    "\n",
    "- For a given observation, the kernel value tell us the other points that are \"nearby\".\n",
    "- Points that are near each other should have similar values.\n",
    "- The 'difficult' points that are far away from the rest exert the most influence on predictions.\n",
    "\n",
    "### Prediction equation\n",
    "\n",
    "For a new input $x$, SVR prediction is:\n",
    "$$\n",
    "\\hat{y}(x)=\\sum_{i \\in SV} w_i\\,K(x_i, x)+b\n",
    "$$\n",
    "\n",
    "Where:\n",
    "- $x_i$: support vectors from training\n",
    "- $K(x_i, x)$: kernel similarity between support vector $x_i$ and new point $x$\n",
    "- $w_i$: learned weights\n",
    "- $b$: intercept\n",
    "\n",
    "### How to calculate one prediction\n",
    "\n",
    "1. Scale the new input using the fitted scaler.\n",
    "2. Compute kernel similarity from the new input to each support vector.\n",
    "3. Multiply each similarity value by its learned weight.\n",
    "4. Sum these weighted similarities.\n",
    "5. Add the intercept $b$.\n",
    "6. Return the result as the predicted value.\n",
    "\n",
    "**Strengths:**\n",
    "- Works well with a good kernel\n",
    "- Handles non-linear patterns\n",
    "- Robust to some outliers\n",
    "\n",
    "**Weaknesses:**\n",
    "- Needs scaling\n",
    "- Sensitive to hyperparameters\n",
    "- Can be slow on larger datasets\n",
    "\n",
    "**Important hyperparameters:**\n",
    "- `C`: controls regularization strength\n",
    "- `epsilon`: width of the no-penalty tube\n",
    "- `kernel`: shape of the regression function\n",
    "- `gamma`: kernel width for RBF kernels\n",
    "\n",
    "**Preprocessing:** scaling is mandatory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ff245799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR RMSE: 5.88 MPa\n",
      "SVR R²:   0.884\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svr_model = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svr',    SVR(kernel='rbf', C=100, epsilon=1.0))\n",
    "])\n",
    "svr_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_svr = svr_model.predict(X_test)\n",
    "svr_rmse   = root_mean_squared_error(y_test, y_pred_svr)\n",
    "svr_r2     = r2_score(y_test, y_pred_svr)\n",
    "\n",
    "print(f'SVR RMSE: {svr_rmse:.2f} MPa')\n",
    "print(f'SVR R²:   {svr_r2:.3f}')\n",
    "results['SVR'] = {'rmse': svr_rmse, 'r2': svr_r2}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1b11571",
   "metadata": {},
   "source": [
    "## 8. Gaussian process regression (GPR)\n",
    "\n",
    "GPR predicts a full distribution at each point, not just one number.\n",
    "\n",
    "In plain language:\n",
    "- The kernel quantifies how strongly each training point should influence another point.\n",
    "- The model combines those influences to get a mean prediction.\n",
    "- It also returns uncertainty, which usually grows in regions with less similar training data.\n",
    "\n",
    "### Inference equation\n",
    "\n",
    "For a new point $x_*$, GPR computes the mean prediction as:\n",
    "$$\n",
    "\\mu_* = K(x_*, X)\\,[K(X, X)+\\sigma_n^2 I]^{-1} y\n",
    "$$\n",
    "\n",
    "Where:\n",
    "- $\\mu_*$ is the predicted mean\n",
    "- $K(\\cdot,\\cdot)$ the chosen kernel\n",
    "- $X, y$ are training inputs and targets\n",
    "- $\\sigma_n^2$ is noise level, captured by `WhiteKernel` or `alpha`\n",
    "- $I$ is the identity matrix\n",
    "\n",
    "The same covariance terms can also be used to calculate the variability, or uncertainty, around that prediction.\n",
    "\n",
    "### What is learned during training\n",
    "\n",
    "The free parameters in the kernel function are optimized so that the output from the model aligns with the training data. In the example RBF kernel function above, the gamma parameter would be optimized. In the GPR example below, the main learned parameters are the Matérn length scale and the `WhiteKernel` noise level. Scikit-learn chooses them by maximizing the log marginal likelihood of the training data.\n",
    "\n",
    "The log marginal likelihood is the log probability of observing the training data values with the current kernel settings. It tells us how well the kernel explains the data. Scikit-learn calculates it from the covariance matrix and uses that score to decide which kernel parameters make the data most probable.\n",
    "\n",
    "The code cell below plots the log marginal likelihood as a function of length scale for one feature, so you can see the optimization idea directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a274bf98",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best length scale in this demo: 0.011\n",
      "Best log marginal likelihood:   -5573.632\n"
     ]
    }
   ],
   "source": [
    "# Visualize how the training likelihood changes as the length scale changes.\n",
    "# This one-feature demo keeps the picture simple while using the learned noise level from the full model.\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import Matern, WhiteKernel\n",
    "\n",
    "age_feature_index = feature_names.index('age')\n",
    "X_train_gpr_demo = X_train_scaled[:, [age_feature_index]]\n",
    "\n",
    "learned_noise_level = np.float64(0.04063645443470903)\n",
    "gpr_demo_kernel = Matern(length_scale=1.0, length_scale_bounds=(1e-2, 1e2), nu=1.5) + WhiteKernel(\n",
    "    noise_level=learned_noise_level,\n",
    "    noise_level_bounds='fixed'\n",
    ")\n",
    "\n",
    "gpr_demo = GaussianProcessRegressor(\n",
    "    kernel=gpr_demo_kernel,\n",
    "    optimizer=None,\n",
    "    normalize_y=True,\n",
    ")\n",
    "gpr_demo.fit(X_train_gpr_demo, y_train)\n",
    "\n",
    "length_scales = np.logspace(-2, 2, 200)\n",
    "log_marginal_likelihood = np.array([\n",
    "    gpr_demo.log_marginal_likelihood(theta=np.log([length_scale]))\n",
    "    for length_scale in length_scales\n",
    "])\n",
    "\n",
    "best_idx = np.argmax(log_marginal_likelihood)\n",
    "best_length_scale = length_scales[best_idx]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.semilogx(length_scales, log_marginal_likelihood, color='teal', linewidth=2)\n",
    "ax.axvline(best_length_scale, color='black', linestyle='--', linewidth=1)\n",
    "ax.scatter([best_length_scale], [log_marginal_likelihood[best_idx]], color='crimson', zorder=3)\n",
    "ax.set_xlabel('Length scale')\n",
    "ax.set_ylabel('Log marginal likelihood')\n",
    "ax.set_title(f'Likelihood vs length scale for {feature_names[age_feature_index]}')\n",
    "ax.grid(alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f'Best length scale in this demo: {best_length_scale:.3f}')\n",
    "print(f'Best log marginal likelihood:   {log_marginal_likelihood[best_idx]:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d0580d",
   "metadata": {},
   "source": [
    "### How to calculate one prediction\n",
    "\n",
    "1. Scale the new input with the fitted scaler.\n",
    "2. Compute kernel similarities between the new point and all training points.\n",
    "3. Use the trained covariance term $[K(X, X)+\\sigma_n^2 I]^{-1}$.\n",
    "4. Combine those learned covariance terms to get the mean prediction.\n",
    "5. Compute $\\sigma_*^2$ to get uncertainty at that point.\n",
    "6. Report prediction as mean plus uncertainty (for example, $\\mu_* \\pm 1.96\\sigma_*$).\n",
    "\n",
    "**Strengths:**\n",
    "- Gives uncertainty estimates\n",
    "- Strong on small datasets\n",
    "- Flexible Bayesian framework\n",
    "\n",
    "**Weaknesses:**\n",
    "- Expensive to train\n",
    "- Kernel choice matters\n",
    "- Does not scale well\n",
    "\n",
    "**Important hyperparameters:**\n",
    "- `length_scale`: width of the kernel\n",
    "- `kernel`: function prior, such as Matérn or RBF\n",
    "- `n_restarts_optimizer`: extra kernel optimization attempts\n",
    "- `alpha`: stability term added to the covariance matrix\n",
    "- `WhiteKernel`: learns the noise level in the data\n",
    "\n",
    "**Preprocessing:** scaling is recommended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4ee08fc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           GPR RMSE: 4.48 MPa\n",
      "             GPR R²: 0.933\n",
      "Mean prediction std: 4.66 MPa\n"
     ]
    }
   ],
   "source": [
    "scaler_gpr     = StandardScaler()\n",
    "X_train_scaled = scaler_gpr.fit_transform(X_train)\n",
    "X_test_scaled  = scaler_gpr.transform(X_test)\n",
    "\n",
    "kernel = 1.0 * Matern(length_scale=np.ones(X_train.shape[1]), length_scale_bounds=(1e-2, 1e2), nu=1.5) + WhiteKernel(noise_level=1.0)\n",
    "\n",
    "gpr_model = GaussianProcessRegressor(\n",
    "    kernel=kernel,\n",
    "    n_restarts_optimizer=10,\n",
    "    random_state=315,\n",
    "    normalize_y=True\n",
    ")\n",
    "\n",
    "gpr_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred_gpr, y_std_gpr = gpr_model.predict(X_test_scaled, return_std=True)\n",
    "gpr_rmse = root_mean_squared_error(y_test, y_pred_gpr)\n",
    "gpr_r2   = r2_score(y_test, y_pred_gpr)\n",
    "\n",
    "print(f'           GPR RMSE: {gpr_rmse:.2f} MPa')\n",
    "print(f'             GPR R²: {gpr_r2:.3f}')\n",
    "print(f'Mean prediction std: {y_std_gpr.mean():.2f} MPa')\n",
    "results['GPR'] = {'rmse': gpr_rmse, 'r2': gpr_r2}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9fad99",
   "metadata": {},
   "source": [
    "## 9. Model comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "53dc5b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model                     RMSE (MPa)       R²\n",
      "---------------------------------------------\n",
      "GPR                             4.48    0.933\n",
      "Optimized random forest         5.18    0.910\n",
      "Random forest                   5.80    0.887\n",
      "SVR                             5.88    0.884\n",
      "KNN                             7.84    0.794\n",
      "Decision tree                   8.61    0.751\n",
      "Linear regression              10.97    0.596\n"
     ]
    }
   ],
   "source": [
    "names = list(results.keys())\n",
    "rmses = [results[n]['rmse'] for n in names]\n",
    "r2s   = [results[n]['r2']   for n in names]\n",
    "\n",
    "order   = np.argsort(rmses)\n",
    "names_s = [names[i] for i in order]\n",
    "rmses_s = [rmses[i] for i in order]\n",
    "r2s_s   = [r2s[i]   for i in order]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
    "\n",
    "ax1.barh(names_s, rmses_s, color='steelblue')\n",
    "ax1.set_xlabel('Test RMSE (MPa) - lower is better')\n",
    "ax1.set_title('Test RMSE by model')\n",
    "\n",
    "ax2.barh(names_s, r2s_s, color='teal')\n",
    "ax2.set_xlabel('Test R² - higher is better')\n",
    "ax2.set_title('Test R² by model')\n",
    "ax2.axvline(0, color='black', linewidth=0.8)\n",
    "\n",
    "plt.suptitle('Concrete compressive strength', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f'{\"Model\":<23} {\"RMSE (MPa)\":>12} {\"R²\":>8}')\n",
    "print('-' * 45)\n",
    "\n",
    "for n, r, r2 in zip(names_s, rmses_s, r2s_s):\n",
    "    print(f'{n:<23} {r:>12.2f} {r2:>8.3f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "522d41b5",
   "metadata": {},
   "source": [
    "## 10. Poisson regression\n",
    "\n",
    "Poisson regression is a linear model for non-negative count data. It is a good match when the target is a count rather than a continuous value.\n",
    "\n",
    "### Prediction equation\n",
    "\n",
    "$$\n",
    "\\log(\\lambda(x)) = \\mathbf{w}^\\top \\mathbf{x} + b\n",
    "$$\n",
    "$$\n",
    "\\hat{y}=\\lambda(x)=\\exp(\\mathbf{w}^\\top \\mathbf{x} + b)\n",
    "$$\n",
    "\n",
    "### What is learned during training\n",
    "\n",
    "- One coefficient for each feature in $\\mathbf{w}$\n",
    "- One intercept value $b$\n",
    "- Coefficients are estimated by maximizing penalized Poisson likelihood\n",
    "\n",
    "### How to calculate one prediction\n",
    "\n",
    "1. Scale the new sample with the fitted scaler.\n",
    "2. Compute the linear score $\\eta = \\mathbf{w}^\\top \\mathbf{x} + b$.\n",
    "3. Convert to the count rate with $\\exp(\\eta)$.\n",
    "4. Return that non-negative expected count.\n",
    "\n",
    "**Strengths:**\n",
    "- Designed for counts\n",
    "- Non-negative predictions\n",
    "- Fast and interpretable\n",
    "\n",
    "**Weaknesses:**\n",
    "- Only for count targets\n",
    "- Limited flexibility\n",
    "- Assumes a Poisson-style mean/variance relationship\n",
    "\n",
    "**Important hyperparameters:**\n",
    "- `alpha`: L2 regularization strength\n",
    "- `max_iter`: solver iteration limit\n",
    "\n",
    "**Preprocessing:** scaling is recommended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "96ac91b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (5190, 12)\n",
      "\n",
      "Target distribution (visit counts):\n",
      "visits\n",
      "0    4141\n",
      "1     782\n",
      "2     174\n",
      "3      30\n",
      "4      24\n",
      "5       9\n",
      "6      12\n",
      "7      12\n",
      "8       5\n",
      "9       1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visits</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>illness</th>\n",
       "      <th>reduced</th>\n",
       "      <th>health</th>\n",
       "      <th>private</th>\n",
       "      <th>freepoor</th>\n",
       "      <th>freerepat</th>\n",
       "      <th>nchronic</th>\n",
       "      <th>lchronic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.90</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   visits  gender   age  income  illness  reduced  health  private  freepoor  \\\n",
       "0       1       0  0.19    0.55        1        4       1        1         0   \n",
       "1       1       0  0.19    0.45        1        2       1        1         0   \n",
       "2       1       1  0.19    0.90        3        0       0        0         0   \n",
       "3       1       1  0.19    0.15        1        0       0        0         0   \n",
       "4       1       1  0.19    0.45        2        5       1        0         0   \n",
       "\n",
       "   freerepat  nchronic  lchronic  \n",
       "0          0         0         0  \n",
       "1          0         0         0  \n",
       "2          0         0         0  \n",
       "3          0         0         0  \n",
       "4          0         1         0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/AER/DoctorVisits.csv'\n",
    "df_v = pd.read_csv(url).drop(columns=['rownames'])\n",
    "\n",
    "# Encode binary string columns as 0/1\n",
    "df_v['gender']    = (df_v['gender']    == 'male').astype(int)\n",
    "df_v['private']   = (df_v['private']   == 'yes').astype(int)\n",
    "df_v['freepoor']  = (df_v['freepoor']  == 'yes').astype(int)\n",
    "df_v['freerepat'] = (df_v['freerepat'] == 'yes').astype(int)\n",
    "df_v['nchronic']  = (df_v['nchronic']  == 'yes').astype(int)\n",
    "df_v['lchronic']  = (df_v['lchronic']  == 'yes').astype(int)\n",
    "\n",
    "print(f'Shape: {df_v.shape}')\n",
    "print()\n",
    "print('Target distribution (visit counts):')\n",
    "print(df_v['visits'].value_counts().sort_index().to_string())\n",
    "df_v.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d8aee3e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n",
    "\n",
    "axes[0].bar(\n",
    "    df_v['visits'].value_counts().sort_index().index,\n",
    "    df_v['visits'].value_counts().sort_index().values,\n",
    "    color='steelblue', edgecolor='white'\n",
    ")\n",
    "\n",
    "axes[0].set_xlabel('Doctor visits (past 2 weeks)')\n",
    "axes[0].set_ylabel('Count')\n",
    "axes[0].set_title('Target distribution\\n(most respondents had 0 visits)')\n",
    "\n",
    "\n",
    "# Plot number of doctor visits vs number of illnesses using a normalized heatmap\n",
    "heatmap_data = pd.crosstab(df_v['illness'], df_v['visits'], normalize='columns').sort_index(ascending=False)\n",
    "sns.heatmap(heatmap_data, ax=axes[1], cmap='YlGnBu', cbar_kws={'label': 'Proportion'})\n",
    "\n",
    "axes[1].set_xlabel('Number of illnesses (past 2 weeks)')\n",
    "axes[1].set_ylabel('Doctor visits')\n",
    "axes[1].set_title('Visits vs illnesses')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72160124",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Poisson RMSE:          0.705 visits\n",
      "Poisson R²:            0.150\n",
      "Mean predicted visits: 0.306\n",
      "Mean actual visits:    0.284\n",
      "\n",
      "Note: R² is low because 80% of respondents had 0 visits (zero-inflated data).\n",
      "RMSE relative to mean is a better measure: RMSE/mean = 2.48\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import PoissonRegressor\n",
    "\n",
    "feat_v = [c for c in df_v.columns if c != 'visits']\n",
    "X_v    = df_v[feat_v].values\n",
    "y_v    = df_v['visits'].values\n",
    "\n",
    "X_train_v, X_test_v, y_train_v, y_test_v = train_test_split(\n",
    "    X_v, y_v, test_size=0.2, random_state=315\n",
    ")\n",
    "\n",
    "poisson_model = Pipeline([\n",
    "    ('scaler',  StandardScaler()),\n",
    "    ('poisson', PoissonRegressor(alpha=1.0, max_iter=1000))\n",
    "])\n",
    "poisson_model.fit(X_train_v, y_train_v)\n",
    "\n",
    "y_pred_v     = poisson_model.predict(X_test_v)\n",
    "pois_rmse    = root_mean_squared_error(y_test_v, y_pred_v)\n",
    "pois_r2      = r2_score(y_test_v, y_pred_v)\n",
    "mean_pred    = y_pred_v.mean()\n",
    "mean_actual  = y_test_v.mean()\n",
    "\n",
    "print(f'Poisson RMSE:          {pois_rmse:.3f} visits')\n",
    "print(f'Poisson R²:            {pois_r2:.3f}')\n",
    "print(f'Mean predicted visits: {mean_pred:.3f}')\n",
    "print(f'Mean actual visits:    {mean_actual:.3f}')\n",
    "print()\n",
    "print('Note: R² is low because 80% of respondents had 0 visits (zero-inflated data).')\n",
    "print('RMSE relative to mean is a better measure: RMSE/mean =', round(pois_rmse / mean_actual, 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c61b6a7",
   "metadata": {},
   "source": [
    "## 11. SKLearn pipeline optimization\n",
    "\n",
    "Scikit-learn's [`Pipeline()`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) lets you chain together preprocessing steps and a model into a single model-like object. This is great for optimization and deployment because you don't have to manage each individual step in the preprocessing workflow. Downside - it only works with sklearn classes. If you are doing custom feature engineering, you will still need to do those steps outside of the pipeline (or wrap them in a sklearn-like class).\n",
    "\n",
    "### 11.1. PolyFeatures -> PCA -> SCV\n",
    "\n",
    "**Note:** I don't necessarily recommend this combination of preprocessing and model - it's just an example of how to optimize a pipeline with multiple steps and hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5d500b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "# Pipeline: PolynomialFeatures > PCA > StandardScaler > SVM\n",
    "pipeline = Pipeline(\n",
    "    [\n",
    "        ('poly', PolynomialFeatures()),\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('pca', PCA()),\n",
    "        ('svr', SVR())\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "75e0b942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 59.6 s, sys: 515 ms, total: 1min\n",
      "Wall time: 27.6 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
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       "\n",
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       "\n",
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       "a.param-doc-link::before {\n",
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       "/* Fitted state for parameter tooltips */\n",
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       "}\n",
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       ".param-doc-link:hover .param-doc-description {\n",
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       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
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       "  border: 1px dotted var(--sklearn-color-border-box);\n",
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       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
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       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
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       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
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       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
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       "\n",
       ".features .features-container table tr:hover {\n",
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       "\n",
       ".features .features-container table {\n",
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       "\n",
       ".features .features-container table td {\n",
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       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
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       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[(&#x27;poly&#x27;, PolynomialFeatures()),\n",
       "                                       (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;pca&#x27;, PCA()), (&#x27;svr&#x27;, SVR())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid=[{&#x27;pca__n_components&#x27;: [5, 6, 7], &#x27;poly__degree&#x27;: [1],\n",
       "                          &#x27;svr__C&#x27;: [0.25, 0.5, 1.0, 2.0],\n",
       "                          &#x27;svr__epsilon&#x27;: [0.025, 0.05, 0.1, 0.2, 0.4],\n",
       "                          &#x27;svr__kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;]},\n",
       "                         {&#x27;pca__n_components&#x27;: [16, 36, 40, 45],\n",
       "                          &#x27;poly__degree&#x27;: [2], &#x27;svr__C&#x27;: [0.25, 0.5, 1.0, 2.0],\n",
       "                          &#x27;svr__epsilon&#x27;: [0.025, 0.05, 0.1, 0.2, 0.4],\n",
       "                          &#x27;svr__kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;]},\n",
       "                         {&#x27;pca__n_components&#x27;: [32, 64, 128],\n",
       "                          &#x27;poly__degree&#x27;: [3], &#x27;svr__C&#x27;: [0.25, 0.5, 1.0, 2.0],\n",
       "                          &#x27;svr__epsilon&#x27;: [0.025, 0.05, 0.1, 0.2, 0.4],\n",
       "                          &#x27;svr__kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;]}],\n",
       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-estimator;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=estimator,-estimator%20object\">\n",
       "            estimator\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-estimator;\">\n",
       "            estimator: estimator object<br><br>This is assumed to implement the scikit-learn estimator interface.<br>Either estimator needs to provide a ``score`` function,<br>or ``scoring`` must be passed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">Pipeline(step...svr&#x27;, SVR())])</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_grid',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-param_grid;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=param_grid,-dict%20or%20list%20of%20dictionaries\">\n",
       "            param_grid\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-param_grid;\">\n",
       "            param_grid: dict or list of dictionaries<br><br>Dictionary with parameters names (`str`) as keys and lists of<br>parameter settings to try as values, or a list of such<br>dictionaries, in which case the grids spanned by each dictionary<br>in the list are explored. This enables searching over any sequence<br>of parameter settings.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[{&#x27;pca__n_components&#x27;: [5, 6, ...], &#x27;poly__degree&#x27;: [1], &#x27;svr__C&#x27;: [0.25, 0.5, ...], &#x27;svr__epsilon&#x27;: [0.025, 0.05, ...], ...}, {&#x27;pca__n_components&#x27;: [16, 36, ...], &#x27;poly__degree&#x27;: [2], &#x27;svr__C&#x27;: [0.25, 0.5, ...], &#x27;svr__epsilon&#x27;: [0.025, 0.05, ...], ...}, ...]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scoring;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scoring,-str%2C%20callable%2C%20list%2C%20tuple%20or%20dict%2C%20default%3DNone\">\n",
       "            scoring\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scoring;\">\n",
       "            scoring: str, callable, list, tuple or dict, default=None<br><br>Strategy to evaluate the performance of the cross-validated model on<br>the test set.<br><br>If `scoring` represents a single score, one can use:<br><br>- a single string (see :ref:`scoring_string_names`);<br>- a callable (see :ref:`scoring_callable`) that returns a single value;<br>- `None`, the `estimator`&#x27;s<br>  :ref:`default evaluation criterion &lt;scoring_api_overview&gt;` is used.<br><br>If `scoring` represents multiple scores, one can use:<br><br>- a list or tuple of unique strings;<br>- a callable returning a dictionary where the keys are the metric<br>  names and the values are the metric scores;<br>- a dictionary with metric names as keys and callables as values.<br><br>See :ref:`multimetric_grid_search` for an example.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;neg_root_mean_squared_error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.<br><br>.. versionchanged:: v0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-cv;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone\">\n",
       "            cv\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-cv;\">\n",
       "            cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross validation,<br>- integer, to specify the number of folds in a `(Stratified)KFold`,<br>- :term:`CV splitter`,<br>- an iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if the estimator is a classifier and ``y`` is<br>either binary or multiclass, :class:`StratifiedKFold` is used. In all<br>other cases, :class:`KFold` is used. These splitters are instantiated<br>with `shuffle=False` so the splits will be the same across calls.<br><br>Refer :ref:`User Guide &lt;cross_validation&gt;` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-refit;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit,-bool%2C%20str%2C%20or%20callable%2C%20default%3DTrue\">\n",
       "            refit\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-refit;\">\n",
       "            refit: bool, str, or callable, default=True<br><br>Refit an estimator using the best found parameters on the whole<br>dataset.<br><br>For multiple metric evaluation, this needs to be a `str` denoting the<br>scorer that would be used to find the best parameters for refitting<br>the estimator at the end.<br><br>Where there are considerations other than maximum score in<br>choosing a best estimator, ``refit`` can be set to a function which<br>returns the selected ``best_index_`` given ``cv_results_``. In that<br>case, the ``best_estimator_`` and ``best_params_`` will be set<br>according to the returned ``best_index_`` while the ``best_score_``<br>attribute will not be available.<br><br>The refitted estimator is made available at the ``best_estimator_``<br>attribute and permits using ``predict`` directly on this<br>``GridSearchCV`` instance.<br><br>Also for multiple metric evaluation, the attributes ``best_index_``,<br>``best_score_`` and ``best_params_`` will only be available if<br>``refit`` is set and all of them will be determined w.r.t this specific<br>scorer.<br><br>See ``scoring`` parameter to know more about multiple metric<br>evaluation.<br><br>See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`<br>to see how to design a custom selection strategy using a callable<br>via `refit`.<br><br>See :ref:`this example<br>&lt;sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py&gt;`<br>for an example of how to use ``refit=callable`` to balance model<br>complexity and cross-validated score.<br><br>.. versionchanged:: 0.20<br>    Support for callable added.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Controls the verbosity of information printed during fitting, with higher<br>values yielding more detailed logging.<br><br>- 0 : no messages are printed;<br>- &gt;=1 : summary of the total number of fits;<br>- &gt;=2 : computation time for each fold and parameter candidate;<br>- &gt;=3 : fold indices and scores;<br>- &gt;=10 : parameter candidate indices and START messages before each fit.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-pre_dispatch;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=pre_dispatch,-int%2C%20or%20str%2C%20default%3D%272%2An_jobs%27\">\n",
       "            pre_dispatch\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-pre_dispatch;\">\n",
       "            pre_dispatch: int, or str, default=&#x27;2*n_jobs&#x27;<br><br>Controls the number of jobs that get dispatched during parallel<br>execution. Reducing this number can be useful to avoid an<br>explosion of memory consumption when more jobs get dispatched<br>than CPUs can process. This parameter can be:<br><br>- None, in which case all the jobs are immediately created and spawned. Use<br>  this for lightweight and fast-running jobs, to avoid delays due to on-demand<br>  spawning of the jobs<br>- An int, giving the exact number of total jobs that are spawned<br>- A str, giving an expression as a function of n_jobs, as in &#x27;2*n_jobs&#x27;</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-error_score;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=error_score,-%27raise%27%20or%20numeric%2C%20default%3Dnp.nan\">\n",
       "            error_score\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-error_score;\">\n",
       "            error_score: &#x27;raise&#x27; or numeric, default=np.nan<br><br>Value to assign to the score if an error occurs in estimator fitting.<br>If set to &#x27;raise&#x27;, the error is raised. If a numeric value is given,<br>FitFailedWarning is raised. This parameter does not affect the refit<br>step, which will always raise the error.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-return_train_score;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=return_train_score,-bool%2C%20default%3DFalse\">\n",
       "            return_train_score\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-return_train_score;\">\n",
       "            return_train_score: bool, default=False<br><br>If ``False``, the ``cv_results_`` attribute will not include training<br>scores.<br>Computing training scores is used to get insights on how different<br>parameter settings impact the overfitting/underfitting trade-off.<br>However computing the scores on the training set can be computationally<br>expensive and is not strictly required to select the parameters that<br>yield the best generalization performance.<br><br>.. versionadded:: 0.19<br><br>.. versionchanged:: 0.21<br>    Default value was changed from ``True`` to ``False``</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_estimator_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_estimator_,-estimator\">\n",
       "            best_estimator_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_estimator_;\">\n",
       "            best_estimator_: estimator<br><br>Estimator that was chosen by the search, i.e. estimator<br>which gave highest score (or smallest loss if specified)<br>on the left out data. Not available if ``refit=False``.<br><br>See ``refit`` parameter for more information on allowed values.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">Pipeline</td>\n",
       "           <td>Pipeline(step...l=&#x27;linear&#x27;))])</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_index_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_index_,-int\">\n",
       "            best_index_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_index_;\">\n",
       "            best_index_: int<br><br>The index (of the ``cv_results_`` arrays) which corresponds to the best<br>candidate parameter setting.<br><br>The dict at ``search.cv_results_[&#x27;params&#x27;][search.best_index_]`` gives<br>the parameter setting for the best model, that gives the highest<br>mean score (``search.best_score_``).<br><br>For multi-metric evaluation, this is present only if ``refit`` is<br>specified.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int64</td>\n",
       "           <td>np.int64(796)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_params_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_params_,-dict\">\n",
       "            best_params_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_params_;\">\n",
       "            best_params_: dict<br><br>Parameter setting that gave the best results on the hold out data.<br><br>For multi-metric evaluation, this is present only if ``refit`` is<br>specified.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;pc...ts&#x27;: 128, &#x27;po...ee&#x27;: 3, &#x27;svr__C&#x27;: 2.0, &#x27;sv...on&#x27;: 0.4, ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_score_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_score_,-float\">\n",
       "            best_score_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_score_;\">\n",
       "            best_score_: float<br><br>Mean cross-validated score of the best_estimator<br><br>For multi-metric evaluation, this is present only if ``refit`` is<br>specified.<br><br>This attribute is not available if ``refit`` is a function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>-6.937</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-cv_results_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv_results_,-dict%20of%20numpy%20%28masked%29%20ndarrays\">\n",
       "            cv_results_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-cv_results_;\">\n",
       "            cv_results_: dict of numpy (masked) ndarrays<br><br>A dict with keys as column headers and values as columns, that can be<br>imported into a pandas ``DataFrame``.<br><br>For instance the below given table<br><br>+------------+-----------+------------+-----------------+---+---------+<br>|param_kernel|param_gamma|param_degree|split0_test_score|...|rank_t...|<br>+============+===========+============+=================+===+=========+<br>|  &#x27;poly&#x27;    |     --    |      2     |       0.80      |...|    2    |<br>+------------+-----------+------------+-----------------+---+---------+<br>|  &#x27;poly&#x27;    |     --    |      3     |       0.70      |...|    4    |<br>+------------+-----------+------------+-----------------+---+---------+<br>|  &#x27;rbf&#x27;     |     0.1   |     --     |       0.80      |...|    3    |<br>+------------+-----------+------------+-----------------+---+---------+<br>|  &#x27;rbf&#x27;     |     0.2   |     --     |       0.93      |...|    1    |<br>+------------+-----------+------------+-----------------+---+---------+<br><br>will be represented by a ``cv_results_`` dict of::<br><br>    {<br>    &#x27;param_kernel&#x27;: masked_array(data = [&#x27;poly&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;rbf&#x27;],<br>                                 mask = [False False False False]...)<br>    &#x27;param_gamma&#x27;: masked_array(data = [-- -- 0.1 0.2],<br>                                mask = [ True  True False False]...),<br>    &#x27;param_degree&#x27;: masked_array(data = [2.0 3.0 -- --],<br>                                 mask = [False False  True  True]...),<br>    &#x27;split0_test_score&#x27;  : [0.80, 0.70, 0.80, 0.93],<br>    &#x27;split1_test_score&#x27;  : [0.82, 0.50, 0.70, 0.78],<br>    &#x27;mean_test_score&#x27;    : [0.81, 0.60, 0.75, 0.85],<br>    &#x27;std_test_score&#x27;     : [0.01, 0.10, 0.05, 0.08],<br>    &#x27;rank_test_score&#x27;    : [2, 4, 3, 1],<br>    &#x27;split0_train_score&#x27; : [0.80, 0.92, 0.70, 0.93],<br>    &#x27;split1_train_score&#x27; : [0.82, 0.55, 0.70, 0.87],<br>    &#x27;mean_train_score&#x27;   : [0.81, 0.74, 0.70, 0.90],<br>    &#x27;std_train_score&#x27;    : [0.01, 0.19, 0.00, 0.03],<br>    &#x27;mean_fit_time&#x27;      : [0.73, 0.63, 0.43, 0.49],<br>    &#x27;std_fit_time&#x27;       : [0.01, 0.02, 0.01, 0.01],<br>    &#x27;mean_score_time&#x27;    : [0.01, 0.06, 0.04, 0.04],<br>    &#x27;std_score_time&#x27;     : [0.00, 0.00, 0.00, 0.01],<br>    &#x27;params&#x27;             : [{&#x27;kernel&#x27;: &#x27;poly&#x27;, &#x27;degree&#x27;: 2}, ...],<br>    }<br><br>For an example of visualization and interpretation of GridSearch results,<br>see :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py`.<br><br>NOTE<br><br>The key ``&#x27;params&#x27;`` is used to store a list of parameter<br>settings dicts for all the parameter candidates.<br><br>The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and<br>``std_score_time`` are all in seconds.<br><br>For multi-metric evaluation, the scores for all the scorers are<br>available in the ``cv_results_`` dict at the keys ending with that<br>scorer&#x27;s name (``&#x27;_&lt;scorer_name&gt;&#x27;``) instead of ``&#x27;_score&#x27;`` shown<br>above. (&#x27;split0_test_precision&#x27;, &#x27;mean_train_precision&#x27; etc.)</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;me...me&#x27;: array([0.0262..., 0.11273726]), &#x27;me...me&#x27;: array([0.0069..., 0.02279266]), &#x27;me...re&#x27;: array([-12.45...-12.62217883]), &#x27;pa...ts&#x27;: masked_array(..._value=999999), ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-multimetric_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=multimetric_,-bool\">\n",
       "            multimetric_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-multimetric_;\">\n",
       "            multimetric_: bool<br><br>Whether or not the scorers compute several metrics.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`. Only defined if<br>`best_estimator_` is defined (see the documentation for the `refit`<br>parameter for more details) and that `best_estimator_` exposes<br>`n_features_in_` when fit.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>8</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_splits_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_splits_,-int\">\n",
       "            n_splits_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_splits_;\">\n",
       "            n_splits_: int<br><br>The number of cross-validation splits (folds/iterations).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>3</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-refit_time_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit_time_,-float\">\n",
       "            refit_time_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-refit_time_;\">\n",
       "            refit_time_: float<br><br>Seconds used for refitting the best model on the whole dataset.<br><br>This is present only if ``refit`` is not False.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float</td>\n",
       "           <td>2.2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scorer_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scorer_,-function%20or%20a%20dict\">\n",
       "            scorer_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scorer_;\">\n",
       "            scorer_: function or a dict<br><br>Scorer function used on the held out data to choose the best<br>parameters for the model.<br><br>For multi-metric evaluation, this attribute holds the validated<br>``scoring`` dict which maps the scorer key to the scorer callable.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">_Scorer</td>\n",
       "           <td>make_scorer(r...hod=&#x27;predict&#x27;)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PolynomialFeatures</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___poly__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('degree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-degree;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=degree,-int%20or%20tuple%20%28min_degree%2C%20max_degree%29%2C%20default%3D2\">\n",
       "            degree\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-degree;\">\n",
       "            degree: int or tuple (min_degree, max_degree), default=2<br><br>If a single int is given, it specifies the maximal degree of the<br>polynomial features. If a tuple `(min_degree, max_degree)` is passed,<br>then `min_degree` is the minimum and `max_degree` is the maximum<br>polynomial degree of the generated features. Note that `min_degree=0`<br>and `min_degree=1` are equivalent as outputting the degree zero term is<br>determined by `include_bias`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('interaction_only',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-interaction_only;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=interaction_only,-bool%2C%20default%3DFalse\">\n",
       "            interaction_only\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-interaction_only;\">\n",
       "            interaction_only: bool, default=False<br><br>If `True`, only interaction features are produced: features that are<br>products of at most `degree` *distinct* input features, i.e. terms with<br>power of 2 or higher of the same input feature are excluded:<br><br>- included: `x[0]`, `x[1]`, `x[0] * x[1]`, etc.<br>- excluded: `x[0] ** 2`, `x[0] ** 2 * x[1]`, etc.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('include_bias',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-include_bias;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=include_bias,-bool%2C%20default%3DTrue\">\n",
       "            include_bias\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-include_bias;\">\n",
       "            include_bias: bool, default=True<br><br>If `True` (default), then include a bias column, the feature in which<br>all polynomial powers are zero (i.e. a column of ones - acts as an<br>intercept term in a linear model).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('order',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-order;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=order,-%7B%27C%27%2C%20%27F%27%7D%2C%20default%3D%27C%27\">\n",
       "            order\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-order;\">\n",
       "            order: {&#x27;C&#x27;, &#x27;F&#x27;}, default=&#x27;C&#x27;<br><br>Order of output array in the dense case. `&#x27;F&#x27;` order is faster to<br>compute, but may slow down subsequent estimators.<br><br>.. versionadded:: 0.21</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;C&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>8</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_output_features_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=n_output_features_,-int\">\n",
       "            n_output_features_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_output_features_;\">\n",
       "            n_output_features_: int<br><br>The total number of polynomial output features. The number of output<br>features is computed by iterating over all suitably sized combinations<br>of input features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>165</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-powers_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=powers_,-ndarray%20of%20shape%20%28n_output_features_%2C%20n_features_in_%29\">\n",
       "            powers_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-powers_;\">\n",
       "            powers_: ndarray of shape (`n_output_features_`, `n_features_in_`)<br><br>`powers_[i, j]` is the exponent of the jth input in the ith output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](165, 8)</td>\n",
       "           <td>[[0,0,0,...,0,0,0],\n",
       " [1,0,0,...,0,0,0],\n",
       " [0,1,0,...,0,0,0],\n",
       " ...,\n",
       " [0,0,0,...,0,2,1],\n",
       " [0,0,0,...,0,1,2],\n",
       " [0,0,0,...,0,0,3]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>165 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x7^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_mean;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_mean;\">\n",
       "            with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_std;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_std;\">\n",
       "            with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-mean_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=mean_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            mean_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-mean_;\">\n",
       "            mean_: ndarray of shape (n_features,) or None<br><br>The mean value for each feature in the training set.<br>Equal to ``None`` when ``with_mean=False`` and ``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](165,)</td>\n",
       "           <td>[       1.  ,     278.71,      73.18,...,26003894.4 , 4338416.12,\n",
       "  1454857.08]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>165</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_seen_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_samples_seen_,-int%20or%20ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            n_samples_seen_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_seen_;\">\n",
       "            n_samples_seen_: int or ndarray of shape (n_features,)<br><br>The number of samples processed by the estimator for each feature.<br>If there are no missing samples, the ``n_samples_seen`` will be an<br>integer, otherwise it will be an array of dtype int. If<br>`sample_weights` are used it will be a float (if no missing data)<br>or an array of dtype float that sums the weights seen so far.<br>Will be reset on new calls to fit, but increments across<br>``partial_fit`` calls.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>824</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scale_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=scale_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            scale_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scale_;\">\n",
       "            scale_: ndarray of shape (n_features,) or None<br><br>Per feature relative scaling of the data to achieve zero mean and unit<br>variance. Generally this is calculated using `np.sqrt(var_)`. If a<br>variance is zero, we can&#x27;t achieve unit variance, and the data is left<br>as-is, giving a scaling factor of 1. `scale_` is equal to `None`<br>when `with_std=False`.<br><br>.. versionadded:: 0.17<br>   *scale_*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](165,)</td>\n",
       "           <td>[       1.  ,     105.02,      86.86,...,34568934.77,14365970.25,\n",
       "  6956995.54]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-var_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=var_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            var_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-var_;\">\n",
       "            var_: ndarray of shape (n_features,) or None<br><br>The variance for each feature in the training set. Used to compute<br>`scale_`. Equal to ``None`` when ``with_mean=False`` and<br>``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](165,)</td>\n",
       "           <td>[0.00e+00,1.10e+04,7.54e+03,...,1.20e+15,2.06e+14,4.84e+13]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>165 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>x0</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x8</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x9</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x10</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x11</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x12</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x13</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x14</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x15</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x16</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x17</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x18</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x19</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x20</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x21</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x22</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x23</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x24</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x25</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x26</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x27</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x28</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x29</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x30</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x31</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x32</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x33</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x34</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x35</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x36</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x37</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x38</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x39</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x40</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x41</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x42</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x43</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x44</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x45</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x46</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x47</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x48</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x49</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x50</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x51</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x52</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x53</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x54</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x55</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x56</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x57</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x58</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x59</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x60</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x61</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x62</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x63</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x64</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x65</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x66</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x67</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x68</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x69</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x70</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x71</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x72</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x73</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x74</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x75</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x76</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x77</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x78</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x79</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x80</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x81</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x82</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x83</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x84</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x85</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x86</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x87</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x88</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x89</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x90</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x91</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x92</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x93</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x94</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x95</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x96</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x97</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x98</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x99</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x100</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x101</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x102</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x103</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x104</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x105</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x106</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x107</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x108</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x109</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x110</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x111</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x112</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x113</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x114</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x115</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x116</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x117</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x118</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x119</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x120</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x121</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x122</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x123</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x124</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x125</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x126</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x127</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x128</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x129</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x130</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x131</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x132</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x133</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x134</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x135</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x136</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x137</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x138</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x139</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x140</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x141</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x142</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x143</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x144</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x145</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x146</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x147</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x148</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x149</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x150</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x151</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x152</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x153</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x154</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x155</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x156</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x157</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x158</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x159</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x160</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x161</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x162</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x163</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x164</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PCA</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___pca__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_components',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_components;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_components,-int%2C%20float%20or%20%27mle%27%2C%20default%3DNone\">\n",
       "            n_components\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_components;\">\n",
       "            n_components: int, float or &#x27;mle&#x27;, default=None<br><br>Number of components to keep.<br>if n_components is not set all components are kept::<br><br>    n_components == min(n_samples, n_features)<br><br>If ``n_components == &#x27;mle&#x27;`` and ``svd_solver == &#x27;full&#x27;``, Minka&#x27;s<br>MLE is used to guess the dimension. Use of ``n_components == &#x27;mle&#x27;``<br>will interpret ``svd_solver == &#x27;auto&#x27;`` as ``svd_solver == &#x27;full&#x27;``.<br><br>If ``0 &lt; n_components &lt; 1`` and ``svd_solver == &#x27;full&#x27;``, select the<br>number of components such that the amount of variance that needs to be<br>explained is greater than the percentage specified by n_components.<br><br>If ``svd_solver == &#x27;arpack&#x27;``, the number of components must be<br>strictly less than the minimum of n_features and n_samples.<br><br>Hence, the None case results in::<br><br>    n_components == min(n_samples, n_features) - 1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">128</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If False, data passed to fit are overwritten and running<br>fit(X).transform(X) will not yield the expected results,<br>use fit_transform(X) instead.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('whiten',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-whiten;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=whiten,-bool%2C%20default%3DFalse\">\n",
       "            whiten\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-whiten;\">\n",
       "            whiten: bool, default=False<br><br>When True (False by default) the `components_` vectors are multiplied<br>by the square root of n_samples and then divided by the singular values<br>to ensure uncorrelated outputs with unit component-wise variances.<br><br>Whitening will remove some information from the transformed signal<br>(the relative variance scales of the components) but can sometime<br>improve the predictive accuracy of the downstream estimators by<br>making their data respect some hard-wired assumptions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('svd_solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-svd_solver;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=svd_solver,-%7B%27auto%27%2C%20%27full%27%2C%20%27covariance_eigh%27%2C%20%27arpack%27%2C%20%27randomized%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27auto%27\">\n",
       "            svd_solver\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-svd_solver;\">\n",
       "            svd_solver: {&#x27;auto&#x27;, &#x27;full&#x27;, &#x27;covariance_eigh&#x27;, &#x27;arpack&#x27;, &#x27;randomized&#x27;},            default=&#x27;auto&#x27;<br><br>&quot;auto&quot; :<br>    The solver is selected by a default &#x27;auto&#x27; policy is based on `X.shape` and<br>    `n_components`: if the input data has fewer than 1000 features and<br>    more than 10 times as many samples, then the &quot;covariance_eigh&quot;<br>    solver is used. Otherwise, if the input data is larger than 500x500<br>    and the number of components to extract is lower than 80% of the<br>    smallest dimension of the data, then the more efficient<br>    &quot;randomized&quot; method is selected. Otherwise the exact &quot;full&quot; SVD is<br>    computed and optionally truncated afterwards.<br>&quot;full&quot; :<br>    Run exact full SVD calling the standard LAPACK solver via<br>    `scipy.linalg.svd` and select the components by postprocessing<br>&quot;covariance_eigh&quot; :<br>    Precompute the covariance matrix (on centered data), run a<br>    classical eigenvalue decomposition on the covariance matrix<br>    typically using LAPACK and select the components by postprocessing.<br>    This solver is very efficient for n_samples &gt;&gt; n_features and small<br>    n_features. It is, however, not tractable otherwise for large<br>    n_features (large memory footprint required to materialize the<br>    covariance matrix). Also note that compared to the &quot;full&quot; solver,<br>    this solver effectively doubles the condition number and is<br>    therefore less numerical stable (e.g. on input data with a large<br>    range of singular values).<br>&quot;arpack&quot; :<br>    Run SVD truncated to `n_components` calling ARPACK solver via<br>    `scipy.sparse.linalg.svds`. It requires strictly<br>    `0 &lt; n_components &lt; min(X.shape)`<br>&quot;randomized&quot; :<br>    Run randomized SVD by the method of Halko et al.<br><br>.. versionadded:: 0.18.0<br><br>.. versionchanged:: 1.5<br>    Added the &#x27;covariance_eigh&#x27; solver.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=tol,-float%2C%20default%3D0.0\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=0.0<br><br>Tolerance for singular values computed by svd_solver == &#x27;arpack&#x27;.<br>Must be of range [0.0, infinity).<br><br>.. versionadded:: 0.18.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('iterated_power',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-iterated_power;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=iterated_power,-int%20or%20%27auto%27%2C%20default%3D%27auto%27\">\n",
       "            iterated_power\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-iterated_power;\">\n",
       "            iterated_power: int or &#x27;auto&#x27;, default=&#x27;auto&#x27;<br><br>Number of iterations for the power method computed by<br>svd_solver == &#x27;randomized&#x27;.<br>Must be of range [0, infinity).<br><br>.. versionadded:: 0.18.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_oversamples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_oversamples;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_oversamples,-int%2C%20default%3D10\">\n",
       "            n_oversamples\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_oversamples;\">\n",
       "            n_oversamples: int, default=10<br><br>This parameter is only relevant when `svd_solver=&quot;randomized&quot;`.<br>It corresponds to the additional number of random vectors to sample the<br>range of `X` so as to ensure proper conditioning. See<br>:func:`~sklearn.utils.extmath.randomized_svd` for more details.<br><br>.. versionadded:: 1.1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_iteration_normalizer',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-power_iteration_normalizer;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=power_iteration_normalizer,-%7B%27auto%27%2C%20%27QR%27%2C%20%27LU%27%2C%20%27none%27%7D%2C%20default%3D%27auto%27\">\n",
       "            power_iteration_normalizer\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-power_iteration_normalizer;\">\n",
       "            power_iteration_normalizer: {&#x27;auto&#x27;, &#x27;QR&#x27;, &#x27;LU&#x27;, &#x27;none&#x27;}, default=&#x27;auto&#x27;<br><br>Power iteration normalizer for randomized SVD solver.<br>Not used by ARPACK. See :func:`~sklearn.utils.extmath.randomized_svd`<br>for more details.<br><br>.. versionadded:: 1.1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Used when the &#x27;arpack&#x27; or &#x27;randomized&#x27; solvers are used. Pass an int<br>for reproducible results across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.<br><br>.. versionadded:: 0.18.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-components_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=components_,-ndarray%20of%20shape%20%28n_components%2C%20n_features%29\">\n",
       "            components_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-components_;\">\n",
       "            components_: ndarray of shape (n_components, n_features)<br><br>Principal axes in feature space, representing the directions of<br>maximum variance in the data. Equivalently, the right singular<br>vectors of the centered input data, parallel to its eigenvectors.<br>The components are sorted by decreasing ``explained_variance_``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](128, 165)</td>\n",
       "           <td>[[ 0.  ,-0.04,-0.05,...,-0.04,-0.06,-0.06],\n",
       " [-0.  , 0.03, 0.12,...,-0.03,-0.03,-0.03],\n",
       " [ 0.  , 0.17,-0.12,..., 0.03, 0.02, 0.02],\n",
       " ...,\n",
       " [-0.  , 0.04,-0.01,...,-0.01, 0.02,-0.  ],\n",
       " [-0.  , 0.03,-0.03,..., 0.07,-0.03, 0.  ],\n",
       " [ 0.  , 0.07, 0.12,...,-0.01, 0.01, 0.  ]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-explained_variance_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=explained_variance_,-ndarray%20of%20shape%20%28n_components%2C%29\">\n",
       "            explained_variance_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-explained_variance_;\">\n",
       "            explained_variance_: ndarray of shape (n_components,)<br><br>The amount of variance explained by each of the selected components.<br>The variance estimation uses `n_samples - 1` degrees of freedom.<br><br>Equal to n_components largest eigenvalues<br>of the covariance matrix of X.<br><br>.. versionadded:: 0.18</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](128,)</td>\n",
       "           <td>[42.59,30.71,23.68,..., 0.  , 0.  , 0.  ]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-explained_variance_ratio_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=explained_variance_ratio_,-ndarray%20of%20shape%20%28n_components%2C%29\">\n",
       "            explained_variance_ratio_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-explained_variance_ratio_;\">\n",
       "            explained_variance_ratio_: ndarray of shape (n_components,)<br><br>Percentage of variance explained by each of the selected components.<br><br>If ``n_components`` is not set then all components are stored and the<br>sum of the ratios is equal to 1.0.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](128,)</td>\n",
       "           <td>[0.26,0.19,0.14,...,0.  ,0.  ,0.  ]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-mean_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=mean_,-ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            mean_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-mean_;\">\n",
       "            mean_: ndarray of shape (n_features,)<br><br>Per-feature empirical mean, estimated from the training set.<br><br>Equal to `X.mean(axis=0)`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](165,)</td>\n",
       "           <td>[ 0., 0.,-0.,...,-0., 0.,-0.]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_components_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_components_,-int\">\n",
       "            n_components_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_components_;\">\n",
       "            n_components_: int<br><br>The estimated number of components. When n_components is set<br>to &#x27;mle&#x27; or a number between 0 and 1 (with svd_solver == &#x27;full&#x27;) this<br>number is estimated from input data. Otherwise it equals the parameter<br>n_components, or the lesser value of n_features and n_samples<br>if n_components is None.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>128</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>165</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_samples_,-int\">\n",
       "            n_samples_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_;\">\n",
       "            n_samples_: int<br><br>Number of samples in the training data.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>824</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-noise_variance_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=noise_variance_,-float\">\n",
       "            noise_variance_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-noise_variance_;\">\n",
       "            noise_variance_: float<br><br>The estimated noise covariance following the Probabilistic PCA model<br>from Tipping and Bishop 1999. See &quot;Pattern Recognition and<br>Machine Learning&quot; by C. Bishop, 12.2.1 p. 574 or<br>http://www.miketipping.com/papers/met-mppca.pdf. It is required to<br>compute the estimated data covariance and score samples.<br><br>Equal to the average of (min(n_features, n_samples) - n_components)<br>smallest eigenvalues of the covariance matrix of X.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>7.915e-07</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-singular_values_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=singular_values_,-ndarray%20of%20shape%20%28n_components%2C%29\">\n",
       "            singular_values_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-singular_values_;\">\n",
       "            singular_values_: ndarray of shape (n_components,)<br><br>The singular values corresponding to each of the selected components.<br>The singular values are equal to the 2-norms of the ``n_components``<br>variables in the lower-dimensional space.<br><br>.. versionadded:: 0.19</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](128,)</td>\n",
       "           <td>[187.21,158.97,139.61,...,  0.07,  0.07,  0.06]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>128 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>pca0</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca8</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca9</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca10</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca11</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca12</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca13</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca14</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca15</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca16</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca17</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca18</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca19</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca20</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca21</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca22</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca23</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca24</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca25</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca26</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca27</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca28</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca29</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca30</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca31</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca32</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca33</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca34</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca35</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca36</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca37</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca38</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca39</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca40</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca41</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca42</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca43</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca44</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca45</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca46</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca47</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca48</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca49</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca50</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca51</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca52</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca53</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca54</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca55</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca56</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca57</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca58</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca59</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca60</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca61</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca62</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca63</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca64</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca65</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca66</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca67</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca68</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca69</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca70</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca71</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca72</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca73</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca74</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca75</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca76</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca77</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca78</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca79</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca80</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca81</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca82</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca83</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca84</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca85</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca86</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca87</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca88</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca89</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca90</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca91</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca92</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca93</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca94</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca95</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca96</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca97</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca98</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca99</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca100</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca101</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca102</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca103</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca104</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca105</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca106</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca107</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca108</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca109</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca110</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca111</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca112</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca113</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca114</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca115</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca116</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca117</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca118</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca119</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca120</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca121</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca122</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca123</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca124</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca125</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca126</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca127</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___svr__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kernel',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-kernel;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=kernel,-%7B%27linear%27%2C%20%27poly%27%2C%20%27rbf%27%2C%20%27sigmoid%27%2C%20%27precomputed%27%7D%20or%20callable%2C%20%20%20%20%20%20%20%20%20%20default%3D%27rbf%27\">\n",
       "            kernel\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-kernel;\">\n",
       "            kernel: {&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;, &#x27;precomputed&#x27;} or callable,          default=&#x27;rbf&#x27;<br><br>Specifies the kernel type to be used in the algorithm.<br>If none is given, &#x27;rbf&#x27; will be used. If a callable is given it is<br>used to precompute the kernel matrix.<br>For an intuitive visualization of different kernel types<br>see :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py`</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;linear&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>The penalty is a squared l2. For an intuitive visualization of the<br>effects of scaling the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">2.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-epsilon;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=epsilon,-float%2C%20default%3D0.1\">\n",
       "            epsilon\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-epsilon;\">\n",
       "            epsilon: float, default=0.1<br><br>Epsilon in the epsilon-SVR model. It specifies the epsilon-tube<br>within which no penalty is associated in the training loss function<br>with points predicted within a distance epsilon from the actual<br>value. Must be non-negative.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.4</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('degree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-degree;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=degree,-int%2C%20default%3D3\">\n",
       "            degree\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-degree;\">\n",
       "            degree: int, default=3<br><br>Degree of the polynomial kernel function (&#x27;poly&#x27;).<br>Must be non-negative. Ignored by all other kernels.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('gamma',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-gamma;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=gamma,-%7B%27scale%27%2C%20%27auto%27%7D%20or%20float%2C%20default%3D%27scale%27\">\n",
       "            gamma\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-gamma;\">\n",
       "            gamma: {&#x27;scale&#x27;, &#x27;auto&#x27;} or float, default=&#x27;scale&#x27;<br><br>Kernel coefficient for &#x27;rbf&#x27;, &#x27;poly&#x27; and &#x27;sigmoid&#x27;.<br><br>- if ``gamma=&#x27;scale&#x27;`` (default) is passed then it uses<br>  1 / (n_features * X.var()) as value of gamma,<br>- if &#x27;auto&#x27;, uses 1 / n_features<br>- if float, must be non-negative.<br><br>.. versionchanged:: 0.22<br>   The default value of ``gamma`` changed from &#x27;auto&#x27; to &#x27;scale&#x27;.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('coef0',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef0;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=coef0,-float%2C%20default%3D0.0\">\n",
       "            coef0\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef0;\">\n",
       "            coef0: float, default=0.0<br><br>Independent term in kernel function.<br>It is only significant in &#x27;poly&#x27; and &#x27;sigmoid&#x27;.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=tol,-float%2C%20default%3D1e-3\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-3<br><br>Tolerance for stopping criterion.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('shrinking',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-shrinking;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=shrinking,-bool%2C%20default%3DTrue\">\n",
       "            shrinking\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-shrinking;\">\n",
       "            shrinking: bool, default=True<br><br>Whether to use the shrinking heuristic.<br>See the :ref:`User Guide &lt;shrinking_svm&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cache_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-cache_size;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=cache_size,-float%2C%20default%3D200\">\n",
       "            cache_size\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-cache_size;\">\n",
       "            cache_size: float, default=200<br><br>Specify the size of the kernel cache (in MB).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">200</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: bool, default=False<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in libsvm that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=max_iter,-int%2C%20default%3D-1\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=-1<br><br>Hard limit on iterations within solver, or -1 for no limit.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=coef_,-ndarray%20or%20sparse%20array/matrix%20of%20shape%20%281%2C%20n_features%29\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            coef_: ndarray or sparse array/matrix of shape (1, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem). This is only available in the case of a linear kernel.<br><br>`coef_` is readonly property derived from `dual_coef_` and<br>`support_vectors_`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1, 128)</td>\n",
       "           <td>[[ 0.09, 1.28, 1.38,...,-0.05, 0.3 ,-0.21]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual_coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=dual_coef_,-ndarray%20or%20sparse%20array/matrix%20of%20shape%20%281%2C%20n_SV%29\">\n",
       "            dual_coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual_coef_;\">\n",
       "            dual_coef_: ndarray or sparse array/matrix of shape (1, n_SV)<br><br>Coefficients of the support vector in the decision function.<br>If `X` is sparse, these will also be sparse.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1, 775)</td>\n",
       "           <td>[[-2.,-2., 2.,...,-2., 2., 2.]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_status_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=fit_status_,-int\">\n",
       "            fit_status_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_status_;\">\n",
       "            fit_status_: int<br><br>0 if correctly fitted, 1 otherwise (will raise warning)</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>0</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            intercept_: ndarray of shape (1,)<br><br>Constants in decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[34.93]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>128</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=n_iter_,-int\">\n",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: int<br><br>Number of iterations run by the optimization routine to fit the model.<br><br>.. versionadded:: 1.1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>42594</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_support_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=n_support_,-ndarray%20of%20shape%20%281%2C%29%2C%20dtype%3Dint32\">\n",
       "            n_support_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_support_;\">\n",
       "            n_support_: ndarray of shape (1,), dtype=int32<br><br>Number of support vectors.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
       "           <td>[775]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-shape_fit_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=shape_fit_,-tuple%20of%20int%20of%20shape%20%28n_dimensions_of_X%2C%29\">\n",
       "            shape_fit_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-shape_fit_;\">\n",
       "            shape_fit_: tuple of int of shape (n_dimensions_of_X,)<br><br>Array dimensions of training vector ``X``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">tuple</td>\n",
       "           <td>(824, 128)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-support_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=support_,-ndarray%20of%20shape%20%281%2C%29%2C%20dtype%3Dint32\">\n",
       "            support_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-support_;\">\n",
       "            support_: ndarray of shape (n_SV,)<br><br>Indices of support vectors.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](775,)</td>\n",
       "           <td>[  0,  1,  2,...,821,822,823]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-support_vectors_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVR.html#:~:text=support_vectors_,-ndarray%20or%20sparse%20array/matrix%20of%20shape%20%28n_SV%2C%20n_features%29\">\n",
       "            support_vectors_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-support_vectors_;\">\n",
       "            support_vectors_: ndarray or sparse array/matrix of shape (n_SV, n_features)<br><br>Support vectors. If `X` is sparse, these will also be sparse.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](775, 128)</td>\n",
       "           <td>[[-10.99, -1.82,  1.05,..., -0.  ,  0.  , -0.  ],\n",
       " [  4.3 , -3.68,  0.3 ,..., -0.  ,  0.  ,  0.  ],\n",
       " [ -6.83,  0.9 , -7.15,...,  0.  ,  0.  ,  0.  ],\n",
       " ...,\n",
       " [  3.82, -3.53,  0.05,...,  0.  , -0.  ,  0.  ],\n",
       " [ -4.85, -4.4 ,  2.77,...,  0.  ,  0.  , -0.  ],\n",
       " [ -4.15, -4.32,  1.39,...,  0.  , -0.  , -0.  ]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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       "\n",
       "/**\n",
       " * Copy the list of feature names formatted as a Python list.\n",
       " *\n",
       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
       " *   contain a `details` element and a table containing feature named.\n",
       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
       " */\n",
       "function copyFeatureNamesToClipboard(element) {\n",
       "    var detailsElem = element.closest('.features').querySelector('details');\n",
       "    var wasOpen = detailsElem.open;\n",
       "    detailsElem.open = true;\n",
       "    var content = element.closest('.features').querySelector('tbody')\n",
       "                  .innerText.trim();\n",
       "    if (!wasOpen) detailsElem.open = false;\n",
       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
       "    const originalStyle = element.style;\n",
       "    const copyMark = document.createElement('span');\n",
       "    copyMark.innerHTML = '✔';\n",
       "    copyMark.style.color = 'blue';\n",
       "    copyMark.style.fontSize = '1em';\n",
       "\n",
       "    navigator.clipboard.writeText(formattedText)\n",
       "        .then(() => {\n",
       "            element.style.display = 'none';\n",
       "            element.parentElement.appendChild(copyMark);\n",
       "\n",
       "            setTimeout(() => {\n",
       "                copyMark.remove();\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'orange';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[('poly', PolynomialFeatures()),\n",
       "                                       ('scaler', StandardScaler()),\n",
       "                                       ('pca', PCA()), ('svr', SVR())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid=[{'pca__n_components': [5, 6, 7], 'poly__degree': [1],\n",
       "                          'svr__C': [0.25, 0.5, 1.0, 2.0],\n",
       "                          'svr__epsilon': [0.025, 0.05, 0.1, 0.2, 0.4],\n",
       "                          'svr__kernel': ['linear', 'poly', 'rbf', 'sigmoid']},\n",
       "                         {'pca__n_components': [16, 36, 40, 45],\n",
       "                          'poly__degree': [2], 'svr__C': [0.25, 0.5, 1.0, 2.0],\n",
       "                          'svr__epsilon': [0.025, 0.05, 0.1, 0.2, 0.4],\n",
       "                          'svr__kernel': ['linear', 'poly', 'rbf', 'sigmoid']},\n",
       "                         {'pca__n_components': [32, 64, 128],\n",
       "                          'poly__degree': [3], 'svr__C': [0.25, 0.5, 1.0, 2.0],\n",
       "                          'svr__epsilon': [0.025, 0.05, 0.1, 0.2, 0.4],\n",
       "                          'svr__kernel': ['linear', 'poly', 'rbf', 'sigmoid']}],\n",
       "             scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Specify the parameter search space as a list of\n",
    "# dictionaries, one for each polynomial degree to test.\n",
    "# This allows us to test different PCA component counts for each degree.\n",
    "params = [\n",
    "    {\n",
    "        'poly__degree': [1], # Degree 1 gives 8 features\n",
    "        'pca__n_components': [5,6,7],\n",
    "        'svr__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
    "        'svr__C': [0.25, 0.50, 1.0, 2.0],\n",
    "        'svr__epsilon': [0.025, 0.05, 0.1, 0.2, 0.4]\n",
    "    },\n",
    "    {\n",
    "        'poly__degree': [2], # Degree 2 gives 46 features\n",
    "        'pca__n_components': [16, 36, 40, 45], #list(range(4,46)),\n",
    "        'svr__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
    "        'svr__C': [0.25, 0.50, 1.0, 2.0],\n",
    "        'svr__epsilon': [0.025, 0.05, 0.1, 0.2, 0.4]\n",
    "    },\n",
    "    {\n",
    "        'poly__degree': [3], # Degree 3 gives 165 features\n",
    "        'pca__n_components': [32, 64, 128], #list(range(4,166)),\n",
    "        'svr__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
    "        'svr__C': [0.25, 0.50, 1.0, 2.0],\n",
    "        'svr__epsilon': [0.025, 0.05, 0.1, 0.2, 0.4]\n",
    "    }\n",
    "]\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    pipeline,\n",
    "    params,\n",
    "    cv=3,\n",
    "    scoring='neg_root_mean_squared_error',\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "02994de5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pca__n_components: 128\n",
      "poly__degree: 3\n",
      "svr__C: 2.0\n",
      "svr__epsilon: 0.4\n",
      "svr__kernel: linear\n"
     ]
    }
   ],
   "source": [
    "best_params = grid_search.best_params_\n",
    "\n",
    "for key, val in best_params.items():\n",
    "    print(f'{key}: {val}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f9cdf15a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimized SVR pipeline RMSE: 6.87 MPa\n",
      "Optimized SVR pipeline R²:   0.842\n"
     ]
    }
   ],
   "source": [
    "winner = grid_search.best_estimator_\n",
    "\n",
    "y_pred_svr = winner.predict(X_test)\n",
    "svr_rmse   = root_mean_squared_error(y_test, y_pred_svr)\n",
    "svr_r2     = r2_score(y_test, y_pred_svr)\n",
    "\n",
    "print(f'Optimized SVR pipeline RMSE: {svr_rmse:.2f} MPa')\n",
    "print(f'Optimized SVR pipeline R²:   {svr_r2:.3f}')\n",
    "results['Optimized SVR pipeline'] = {'rmse': svr_rmse, 'r2': svr_r2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "099c34e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model                     RMSE (MPa)       R²\n",
      "---------------------------------------------\n",
      "GPR                             4.48    0.933\n",
      "Optimized random forest         5.18    0.910\n",
      "Random forest                   5.80    0.887\n",
      "SVR                             5.88    0.884\n",
      "Optimized SVR pipeline          6.87    0.842\n",
      "KNN                             7.84    0.794\n",
      "Decision tree                   8.61    0.751\n",
      "Linear regression              10.97    0.596\n"
     ]
    }
   ],
   "source": [
    "names = list(results.keys())\n",
    "rmses = [results[n]['rmse'] for n in names]\n",
    "r2s   = [results[n]['r2']   for n in names]\n",
    "\n",
    "order   = np.argsort(rmses)\n",
    "names_s = [names[i] for i in order]\n",
    "rmses_s = [rmses[i] for i in order]\n",
    "r2s_s   = [r2s[i]   for i in order]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
    "\n",
    "ax1.barh(names_s, rmses_s, color='steelblue')\n",
    "ax1.set_xlabel('Test RMSE (MPa) - lower is better')\n",
    "ax1.set_title('Test RMSE by model')\n",
    "\n",
    "ax2.barh(names_s, r2s_s, color='teal')\n",
    "ax2.set_xlabel('Test R² - higher is better')\n",
    "ax2.set_title('Test R² by model')\n",
    "ax2.axvline(0, color='black', linewidth=0.8)\n",
    "\n",
    "plt.suptitle('Concrete compressive strength', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f'{\"Model\":<23} {\"RMSE (MPa)\":>12} {\"R²\":>8}')\n",
    "print('-' * 45)\n",
    "\n",
    "for n, r, r2 in zip(names_s, rmses_s, r2s_s):\n",
    "    print(f'{n:<23} {r:>12.2f} {r2:>8.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223dad1b",
   "metadata": {},
   "source": [
    "### 11.2. PolyFeatures -> PCA -> LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9c63e388",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(\n",
    "    [\n",
    "        ('poly', PolynomialFeatures()),\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('pca', PCA()),\n",
    "        ('linear', LinearRegression())\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b2d703bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 39.4 s, sys: 111 ms, total: 39.5 s\n",
      "Wall time: 2.99 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
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       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
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       "*/\n",
       "\n",
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       "\n",
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       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
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       ".features {\n",
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       ".features summary {\n",
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       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
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       "\n",
       ".features details[open] > summary {\n",
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       ".features.fitted details[open] > summary {\n",
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       "\n",
       ".features details > summary .arrow::before {\n",
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       ".features details[open] > summary .arrow::before {\n",
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       ".features details:hover > summary {\n",
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       ".features.fitted details:hover > summary {\n",
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       "\n",
       ".features.fitted .features-container {\n",
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       ".features .image-container {\n",
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       "  align-items: center;\n",
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       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
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       "\n",
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       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
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       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[(&#x27;poly&#x27;, PolynomialFeatures()),\n",
       "                                       (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;pca&#x27;, PCA()),\n",
       "                                       (&#x27;linear&#x27;, LinearRegression())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid=[{&#x27;pca__n_components&#x27;: [4, 5, 6, 7, 8],\n",
       "                          &#x27;poly__degree&#x27;: [1]},\n",
       "                         {&#x27;pca__n_components&#x27;: [15, 16, 17, 18, 19, 20, 21, 22,\n",
       "                                                23, 24, 25, 26, 27, 28, 29, 30,\n",
       "                                                31, 32, 33, 34, 35, 36, 37, 38,\n",
       "                                                39, 40, 41, 42, 43, 44, ...],\n",
       "                          &#x27;poly__degree&#x27;: [2]},\n",
       "                         {&#x27;pca__n_components&#x27;: [105, 107, 109, 111, 113, 115,\n",
       "                                                117, 119, 121, 123, 125, 127,\n",
       "                                                129, 131, 133, 135, 137, 139,\n",
       "                                                141, 143, 145, 147, 149, 151,\n",
       "                                                153, 155, 157, 159, 161, 163, ...],\n",
       "                          &#x27;poly__degree&#x27;: [3]}],\n",
       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-estimator;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=estimator,-estimator%20object\">\n",
       "            estimator\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-estimator;\">\n",
       "            estimator: estimator object<br><br>This is assumed to implement the scikit-learn estimator interface.<br>Either estimator needs to provide a ``score`` function,<br>or ``scoring`` must be passed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">Pipeline(step...egression())])</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_grid',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-param_grid;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=param_grid,-dict%20or%20list%20of%20dictionaries\">\n",
       "            param_grid\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-param_grid;\">\n",
       "            param_grid: dict or list of dictionaries<br><br>Dictionary with parameters names (`str`) as keys and lists of<br>parameter settings to try as values, or a list of such<br>dictionaries, in which case the grids spanned by each dictionary<br>in the list are explored. This enables searching over any sequence<br>of parameter settings.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[{&#x27;pca__n_components&#x27;: [4, 5, ...], &#x27;poly__degree&#x27;: [1]}, {&#x27;pca__n_components&#x27;: [15, 16, ...], &#x27;poly__degree&#x27;: [2]}, ...]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scoring;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scoring,-str%2C%20callable%2C%20list%2C%20tuple%20or%20dict%2C%20default%3DNone\">\n",
       "            scoring\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scoring;\">\n",
       "            scoring: str, callable, list, tuple or dict, default=None<br><br>Strategy to evaluate the performance of the cross-validated model on<br>the test set.<br><br>If `scoring` represents a single score, one can use:<br><br>- a single string (see :ref:`scoring_string_names`);<br>- a callable (see :ref:`scoring_callable`) that returns a single value;<br>- `None`, the `estimator`&#x27;s<br>  :ref:`default evaluation criterion &lt;scoring_api_overview&gt;` is used.<br><br>If `scoring` represents multiple scores, one can use:<br><br>- a list or tuple of unique strings;<br>- a callable returning a dictionary where the keys are the metric<br>  names and the values are the metric scores;<br>- a dictionary with metric names as keys and callables as values.<br><br>See :ref:`multimetric_grid_search` for an example.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;neg_root_mean_squared_error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.<br><br>.. versionchanged:: v0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-cv;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone\">\n",
       "            cv\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-cv;\">\n",
       "            cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross validation,<br>- integer, to specify the number of folds in a `(Stratified)KFold`,<br>- :term:`CV splitter`,<br>- an iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if the estimator is a classifier and ``y`` is<br>either binary or multiclass, :class:`StratifiedKFold` is used. In all<br>other cases, :class:`KFold` is used. These splitters are instantiated<br>with `shuffle=False` so the splits will be the same across calls.<br><br>Refer :ref:`User Guide &lt;cross_validation&gt;` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-refit;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit,-bool%2C%20str%2C%20or%20callable%2C%20default%3DTrue\">\n",
       "            refit\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-refit;\">\n",
       "            refit: bool, str, or callable, default=True<br><br>Refit an estimator using the best found parameters on the whole<br>dataset.<br><br>For multiple metric evaluation, this needs to be a `str` denoting the<br>scorer that would be used to find the best parameters for refitting<br>the estimator at the end.<br><br>Where there are considerations other than maximum score in<br>choosing a best estimator, ``refit`` can be set to a function which<br>returns the selected ``best_index_`` given ``cv_results_``. In that<br>case, the ``best_estimator_`` and ``best_params_`` will be set<br>according to the returned ``best_index_`` while the ``best_score_``<br>attribute will not be available.<br><br>The refitted estimator is made available at the ``best_estimator_``<br>attribute and permits using ``predict`` directly on this<br>``GridSearchCV`` instance.<br><br>Also for multiple metric evaluation, the attributes ``best_index_``,<br>``best_score_`` and ``best_params_`` will only be available if<br>``refit`` is set and all of them will be determined w.r.t this specific<br>scorer.<br><br>See ``scoring`` parameter to know more about multiple metric<br>evaluation.<br><br>See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`<br>to see how to design a custom selection strategy using a callable<br>via `refit`.<br><br>See :ref:`this example<br>&lt;sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py&gt;`<br>for an example of how to use ``refit=callable`` to balance model<br>complexity and cross-validated score.<br><br>.. versionchanged:: 0.20<br>    Support for callable added.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Controls the verbosity of information printed during fitting, with higher<br>values yielding more detailed logging.<br><br>- 0 : no messages are printed;<br>- &gt;=1 : summary of the total number of fits;<br>- &gt;=2 : computation time for each fold and parameter candidate;<br>- &gt;=3 : fold indices and scores;<br>- &gt;=10 : parameter candidate indices and START messages before each fit.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-pre_dispatch;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=pre_dispatch,-int%2C%20or%20str%2C%20default%3D%272%2An_jobs%27\">\n",
       "            pre_dispatch\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-pre_dispatch;\">\n",
       "            pre_dispatch: int, or str, default=&#x27;2*n_jobs&#x27;<br><br>Controls the number of jobs that get dispatched during parallel<br>execution. Reducing this number can be useful to avoid an<br>explosion of memory consumption when more jobs get dispatched<br>than CPUs can process. This parameter can be:<br><br>- None, in which case all the jobs are immediately created and spawned. Use<br>  this for lightweight and fast-running jobs, to avoid delays due to on-demand<br>  spawning of the jobs<br>- An int, giving the exact number of total jobs that are spawned<br>- A str, giving an expression as a function of n_jobs, as in &#x27;2*n_jobs&#x27;</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-error_score;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=error_score,-%27raise%27%20or%20numeric%2C%20default%3Dnp.nan\">\n",
       "            error_score\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-error_score;\">\n",
       "            error_score: &#x27;raise&#x27; or numeric, default=np.nan<br><br>Value to assign to the score if an error occurs in estimator fitting.<br>If set to &#x27;raise&#x27;, the error is raised. If a numeric value is given,<br>FitFailedWarning is raised. This parameter does not affect the refit<br>step, which will always raise the error.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-return_train_score;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=return_train_score,-bool%2C%20default%3DFalse\">\n",
       "            return_train_score\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-return_train_score;\">\n",
       "            return_train_score: bool, default=False<br><br>If ``False``, the ``cv_results_`` attribute will not include training<br>scores.<br>Computing training scores is used to get insights on how different<br>parameter settings impact the overfitting/underfitting trade-off.<br>However computing the scores on the training set can be computationally<br>expensive and is not strictly required to select the parameters that<br>yield the best generalization performance.<br><br>.. versionadded:: 0.19<br><br>.. versionchanged:: 0.21<br>    Default value was changed from ``True`` to ``False``</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_estimator_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_estimator_,-estimator\">\n",
       "            best_estimator_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_estimator_;\">\n",
       "            best_estimator_: estimator<br><br>Estimator that was chosen by the search, i.e. estimator<br>which gave highest score (or smallest loss if specified)<br>on the left out data. Not available if ``refit=False``.<br><br>See ``refit`` parameter for more information on allowed values.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">Pipeline</td>\n",
       "           <td>Pipeline(step...egression())])</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_index_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_index_,-int\">\n",
       "            best_index_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_index_;\">\n",
       "            best_index_: int<br><br>The index (of the ``cv_results_`` arrays) which corresponds to the best<br>candidate parameter setting.<br><br>The dict at ``search.cv_results_[&#x27;params&#x27;][search.best_index_]`` gives<br>the parameter setting for the best model, that gives the highest<br>mean score (``search.best_score_``).<br><br>For multi-metric evaluation, this is present only if ``refit`` is<br>specified.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int64</td>\n",
       "           <td>np.int64(45)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_params_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_params_,-dict\">\n",
       "            best_params_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_params_;\">\n",
       "            best_params_: dict<br><br>Parameter setting that gave the best results on the hold out data.<br><br>For multi-metric evaluation, this is present only if ``refit`` is<br>specified.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;pc...ts&#x27;: 123, &#x27;po...ee&#x27;: 3}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_score_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_score_,-float\">\n",
       "            best_score_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_score_;\">\n",
       "            best_score_: float<br><br>Mean cross-validated score of the best_estimator<br><br>For multi-metric evaluation, this is present only if ``refit`` is<br>specified.<br><br>This attribute is not available if ``refit`` is a function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>-6.444</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-cv_results_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv_results_,-dict%20of%20numpy%20%28masked%29%20ndarrays\">\n",
       "            cv_results_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-cv_results_;\">\n",
       "            cv_results_: dict of numpy (masked) ndarrays<br><br>A dict with keys as column headers and values as columns, that can be<br>imported into a pandas ``DataFrame``.<br><br>For instance the below given table<br><br>+------------+-----------+------------+-----------------+---+---------+<br>|param_kernel|param_gamma|param_degree|split0_test_score|...|rank_t...|<br>+============+===========+============+=================+===+=========+<br>|  &#x27;poly&#x27;    |     --    |      2     |       0.80      |...|    2    |<br>+------------+-----------+------------+-----------------+---+---------+<br>|  &#x27;poly&#x27;    |     --    |      3     |       0.70      |...|    4    |<br>+------------+-----------+------------+-----------------+---+---------+<br>|  &#x27;rbf&#x27;     |     0.1   |     --     |       0.80      |...|    3    |<br>+------------+-----------+------------+-----------------+---+---------+<br>|  &#x27;rbf&#x27;     |     0.2   |     --     |       0.93      |...|    1    |<br>+------------+-----------+------------+-----------------+---+---------+<br><br>will be represented by a ``cv_results_`` dict of::<br><br>    {<br>    &#x27;param_kernel&#x27;: masked_array(data = [&#x27;poly&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;rbf&#x27;],<br>                                 mask = [False False False False]...)<br>    &#x27;param_gamma&#x27;: masked_array(data = [-- -- 0.1 0.2],<br>                                mask = [ True  True False False]...),<br>    &#x27;param_degree&#x27;: masked_array(data = [2.0 3.0 -- --],<br>                                 mask = [False False  True  True]...),<br>    &#x27;split0_test_score&#x27;  : [0.80, 0.70, 0.80, 0.93],<br>    &#x27;split1_test_score&#x27;  : [0.82, 0.50, 0.70, 0.78],<br>    &#x27;mean_test_score&#x27;    : [0.81, 0.60, 0.75, 0.85],<br>    &#x27;std_test_score&#x27;     : [0.01, 0.10, 0.05, 0.08],<br>    &#x27;rank_test_score&#x27;    : [2, 4, 3, 1],<br>    &#x27;split0_train_score&#x27; : [0.80, 0.92, 0.70, 0.93],<br>    &#x27;split1_train_score&#x27; : [0.82, 0.55, 0.70, 0.87],<br>    &#x27;mean_train_score&#x27;   : [0.81, 0.74, 0.70, 0.90],<br>    &#x27;std_train_score&#x27;    : [0.01, 0.19, 0.00, 0.03],<br>    &#x27;mean_fit_time&#x27;      : [0.73, 0.63, 0.43, 0.49],<br>    &#x27;std_fit_time&#x27;       : [0.01, 0.02, 0.01, 0.01],<br>    &#x27;mean_score_time&#x27;    : [0.01, 0.06, 0.04, 0.04],<br>    &#x27;std_score_time&#x27;     : [0.00, 0.00, 0.00, 0.01],<br>    &#x27;params&#x27;             : [{&#x27;kernel&#x27;: &#x27;poly&#x27;, &#x27;degree&#x27;: 2}, ...],<br>    }<br><br>For an example of visualization and interpretation of GridSearch results,<br>see :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py`.<br><br>NOTE<br><br>The key ``&#x27;params&#x27;`` is used to store a list of parameter<br>settings dicts for all the parameter candidates.<br><br>The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and<br>``std_score_time`` are all in seconds.<br><br>For multi-metric evaluation, the scores for all the scorers are<br>available in the ``cv_results_`` dict at the keys ending with that<br>scorer&#x27;s name (``&#x27;_&lt;scorer_name&gt;&#x27;``) instead of ``&#x27;_score&#x27;`` shown<br>above. (&#x27;split0_test_precision&#x27;, &#x27;mean_train_precision&#x27; etc.)</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;me...me&#x27;: array([0.0050..., 0.05662433]), &#x27;me...me&#x27;: array([0.0017..., 0.00627883]), &#x27;me...re&#x27;: array([-13.33... -7.38821679]), &#x27;pa...ts&#x27;: masked_array(..._value=999999), ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-multimetric_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=multimetric_,-bool\">\n",
       "            multimetric_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-multimetric_;\">\n",
       "            multimetric_: bool<br><br>Whether or not the scorers compute several metrics.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`. Only defined if<br>`best_estimator_` is defined (see the documentation for the `refit`<br>parameter for more details) and that `best_estimator_` exposes<br>`n_features_in_` when fit.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>8</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_splits_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_splits_,-int\">\n",
       "            n_splits_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_splits_;\">\n",
       "            n_splits_: int<br><br>The number of cross-validation splits (folds/iterations).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>3</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-refit_time_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit_time_,-float\">\n",
       "            refit_time_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-refit_time_;\">\n",
       "            refit_time_: float<br><br>Seconds used for refitting the best model on the whole dataset.<br><br>This is present only if ``refit`` is not False.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float</td>\n",
       "           <td>2.059</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scorer_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scorer_,-function%20or%20a%20dict\">\n",
       "            scorer_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scorer_;\">\n",
       "            scorer_: function or a dict<br><br>Scorer function used on the held out data to choose the best<br>parameters for the model.<br><br>For multi-metric evaluation, this attribute holds the validated<br>``scoring`` dict which maps the scorer key to the scorer callable.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">_Scorer</td>\n",
       "           <td>make_scorer(r...hod=&#x27;predict&#x27;)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PolynomialFeatures</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___poly__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('degree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-degree;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=degree,-int%20or%20tuple%20%28min_degree%2C%20max_degree%29%2C%20default%3D2\">\n",
       "            degree\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-degree;\">\n",
       "            degree: int or tuple (min_degree, max_degree), default=2<br><br>If a single int is given, it specifies the maximal degree of the<br>polynomial features. If a tuple `(min_degree, max_degree)` is passed,<br>then `min_degree` is the minimum and `max_degree` is the maximum<br>polynomial degree of the generated features. Note that `min_degree=0`<br>and `min_degree=1` are equivalent as outputting the degree zero term is<br>determined by `include_bias`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('interaction_only',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-interaction_only;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=interaction_only,-bool%2C%20default%3DFalse\">\n",
       "            interaction_only\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-interaction_only;\">\n",
       "            interaction_only: bool, default=False<br><br>If `True`, only interaction features are produced: features that are<br>products of at most `degree` *distinct* input features, i.e. terms with<br>power of 2 or higher of the same input feature are excluded:<br><br>- included: `x[0]`, `x[1]`, `x[0] * x[1]`, etc.<br>- excluded: `x[0] ** 2`, `x[0] ** 2 * x[1]`, etc.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('include_bias',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-include_bias;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=include_bias,-bool%2C%20default%3DTrue\">\n",
       "            include_bias\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-include_bias;\">\n",
       "            include_bias: bool, default=True<br><br>If `True` (default), then include a bias column, the feature in which<br>all polynomial powers are zero (i.e. a column of ones - acts as an<br>intercept term in a linear model).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('order',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-order;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=order,-%7B%27C%27%2C%20%27F%27%7D%2C%20default%3D%27C%27\">\n",
       "            order\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-order;\">\n",
       "            order: {&#x27;C&#x27;, &#x27;F&#x27;}, default=&#x27;C&#x27;<br><br>Order of output array in the dense case. `&#x27;F&#x27;` order is faster to<br>compute, but may slow down subsequent estimators.<br><br>.. versionadded:: 0.21</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;C&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>8</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_output_features_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=n_output_features_,-int\">\n",
       "            n_output_features_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_output_features_;\">\n",
       "            n_output_features_: int<br><br>The total number of polynomial output features. The number of output<br>features is computed by iterating over all suitably sized combinations<br>of input features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>165</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-powers_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#:~:text=powers_,-ndarray%20of%20shape%20%28n_output_features_%2C%20n_features_in_%29\">\n",
       "            powers_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-powers_;\">\n",
       "            powers_: ndarray of shape (`n_output_features_`, `n_features_in_`)<br><br>`powers_[i, j]` is the exponent of the jth input in the ith output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](165, 8)</td>\n",
       "           <td>[[0,0,0,...,0,0,0],\n",
       " [1,0,0,...,0,0,0],\n",
       " [0,1,0,...,0,0,0],\n",
       " ...,\n",
       " [0,0,0,...,0,2,1],\n",
       " [0,0,0,...,0,1,2],\n",
       " [0,0,0,...,0,0,3]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>165 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x1 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x3 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x3 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x3 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2 x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x4 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^2 x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x5^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x5 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x5 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5^2 x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x6^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x6 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6^2 x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6 x7^2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x7^3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_mean;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_mean;\">\n",
       "            with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_std;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_std;\">\n",
       "            with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-mean_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=mean_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            mean_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-mean_;\">\n",
       "            mean_: ndarray of shape (n_features,) or None<br><br>The mean value for each feature in the training set.<br>Equal to ``None`` when ``with_mean=False`` and ``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](165,)</td>\n",
       "           <td>[       1.  ,     278.71,      73.18,...,26003894.4 , 4338416.12,\n",
       "  1454857.08]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>165</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_seen_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_samples_seen_,-int%20or%20ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            n_samples_seen_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_seen_;\">\n",
       "            n_samples_seen_: int or ndarray of shape (n_features,)<br><br>The number of samples processed by the estimator for each feature.<br>If there are no missing samples, the ``n_samples_seen`` will be an<br>integer, otherwise it will be an array of dtype int. If<br>`sample_weights` are used it will be a float (if no missing data)<br>or an array of dtype float that sums the weights seen so far.<br>Will be reset on new calls to fit, but increments across<br>``partial_fit`` calls.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>824</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scale_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=scale_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            scale_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scale_;\">\n",
       "            scale_: ndarray of shape (n_features,) or None<br><br>Per feature relative scaling of the data to achieve zero mean and unit<br>variance. Generally this is calculated using `np.sqrt(var_)`. If a<br>variance is zero, we can&#x27;t achieve unit variance, and the data is left<br>as-is, giving a scaling factor of 1. `scale_` is equal to `None`<br>when `with_std=False`.<br><br>.. versionadded:: 0.17<br>   *scale_*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](165,)</td>\n",
       "           <td>[       1.  ,     105.02,      86.86,...,34568934.77,14365970.25,\n",
       "  6956995.54]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-var_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=var_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            var_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-var_;\">\n",
       "            var_: ndarray of shape (n_features,) or None<br><br>The variance for each feature in the training set. Used to compute<br>`scale_`. Equal to ``None`` when ``with_mean=False`` and<br>``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](165,)</td>\n",
       "           <td>[0.00e+00,1.10e+04,7.54e+03,...,1.20e+15,2.06e+14,4.84e+13]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>165 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>x0</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x8</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x9</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x10</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x11</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x12</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x13</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x14</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x15</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x16</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x17</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x18</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x19</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x20</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x21</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x22</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x23</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x24</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x25</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x26</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x27</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x28</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x29</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x30</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x31</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x32</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x33</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x34</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x35</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x36</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x37</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x38</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x39</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x40</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x41</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x42</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x43</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x44</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x45</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x46</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x47</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x48</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x49</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x50</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x51</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x52</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x53</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x54</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x55</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x56</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x57</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x58</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x59</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x60</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x61</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x62</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x63</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x64</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x65</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x66</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x67</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x68</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x69</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x70</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x71</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x72</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x73</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x74</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x75</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x76</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x77</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x78</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x79</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x80</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x81</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x82</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x83</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x84</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x85</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x86</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x87</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x88</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x89</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x90</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x91</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x92</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x93</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x94</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x95</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x96</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x97</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x98</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x99</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x100</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x101</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x102</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x103</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x104</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x105</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x106</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x107</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x108</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x109</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x110</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x111</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x112</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x113</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x114</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x115</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x116</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x117</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x118</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x119</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x120</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x121</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x122</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x123</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x124</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x125</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x126</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x127</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x128</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x129</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x130</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x131</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x132</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x133</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x134</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x135</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x136</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x137</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x138</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x139</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x140</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x141</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x142</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x143</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x144</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x145</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x146</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x147</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x148</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x149</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x150</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x151</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x152</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x153</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x154</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x155</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x156</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x157</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x158</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x159</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x160</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x161</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x162</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x163</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x164</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PCA</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___pca__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_components',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_components;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_components,-int%2C%20float%20or%20%27mle%27%2C%20default%3DNone\">\n",
       "            n_components\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_components;\">\n",
       "            n_components: int, float or &#x27;mle&#x27;, default=None<br><br>Number of components to keep.<br>if n_components is not set all components are kept::<br><br>    n_components == min(n_samples, n_features)<br><br>If ``n_components == &#x27;mle&#x27;`` and ``svd_solver == &#x27;full&#x27;``, Minka&#x27;s<br>MLE is used to guess the dimension. Use of ``n_components == &#x27;mle&#x27;``<br>will interpret ``svd_solver == &#x27;auto&#x27;`` as ``svd_solver == &#x27;full&#x27;``.<br><br>If ``0 &lt; n_components &lt; 1`` and ``svd_solver == &#x27;full&#x27;``, select the<br>number of components such that the amount of variance that needs to be<br>explained is greater than the percentage specified by n_components.<br><br>If ``svd_solver == &#x27;arpack&#x27;``, the number of components must be<br>strictly less than the minimum of n_features and n_samples.<br><br>Hence, the None case results in::<br><br>    n_components == min(n_samples, n_features) - 1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">123</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If False, data passed to fit are overwritten and running<br>fit(X).transform(X) will not yield the expected results,<br>use fit_transform(X) instead.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('whiten',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-whiten;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=whiten,-bool%2C%20default%3DFalse\">\n",
       "            whiten\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-whiten;\">\n",
       "            whiten: bool, default=False<br><br>When True (False by default) the `components_` vectors are multiplied<br>by the square root of n_samples and then divided by the singular values<br>to ensure uncorrelated outputs with unit component-wise variances.<br><br>Whitening will remove some information from the transformed signal<br>(the relative variance scales of the components) but can sometime<br>improve the predictive accuracy of the downstream estimators by<br>making their data respect some hard-wired assumptions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('svd_solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-svd_solver;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=svd_solver,-%7B%27auto%27%2C%20%27full%27%2C%20%27covariance_eigh%27%2C%20%27arpack%27%2C%20%27randomized%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27auto%27\">\n",
       "            svd_solver\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-svd_solver;\">\n",
       "            svd_solver: {&#x27;auto&#x27;, &#x27;full&#x27;, &#x27;covariance_eigh&#x27;, &#x27;arpack&#x27;, &#x27;randomized&#x27;},            default=&#x27;auto&#x27;<br><br>&quot;auto&quot; :<br>    The solver is selected by a default &#x27;auto&#x27; policy is based on `X.shape` and<br>    `n_components`: if the input data has fewer than 1000 features and<br>    more than 10 times as many samples, then the &quot;covariance_eigh&quot;<br>    solver is used. Otherwise, if the input data is larger than 500x500<br>    and the number of components to extract is lower than 80% of the<br>    smallest dimension of the data, then the more efficient<br>    &quot;randomized&quot; method is selected. Otherwise the exact &quot;full&quot; SVD is<br>    computed and optionally truncated afterwards.<br>&quot;full&quot; :<br>    Run exact full SVD calling the standard LAPACK solver via<br>    `scipy.linalg.svd` and select the components by postprocessing<br>&quot;covariance_eigh&quot; :<br>    Precompute the covariance matrix (on centered data), run a<br>    classical eigenvalue decomposition on the covariance matrix<br>    typically using LAPACK and select the components by postprocessing.<br>    This solver is very efficient for n_samples &gt;&gt; n_features and small<br>    n_features. It is, however, not tractable otherwise for large<br>    n_features (large memory footprint required to materialize the<br>    covariance matrix). Also note that compared to the &quot;full&quot; solver,<br>    this solver effectively doubles the condition number and is<br>    therefore less numerical stable (e.g. on input data with a large<br>    range of singular values).<br>&quot;arpack&quot; :<br>    Run SVD truncated to `n_components` calling ARPACK solver via<br>    `scipy.sparse.linalg.svds`. It requires strictly<br>    `0 &lt; n_components &lt; min(X.shape)`<br>&quot;randomized&quot; :<br>    Run randomized SVD by the method of Halko et al.<br><br>.. versionadded:: 0.18.0<br><br>.. versionchanged:: 1.5<br>    Added the &#x27;covariance_eigh&#x27; solver.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=tol,-float%2C%20default%3D0.0\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=0.0<br><br>Tolerance for singular values computed by svd_solver == &#x27;arpack&#x27;.<br>Must be of range [0.0, infinity).<br><br>.. versionadded:: 0.18.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('iterated_power',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-iterated_power;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=iterated_power,-int%20or%20%27auto%27%2C%20default%3D%27auto%27\">\n",
       "            iterated_power\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-iterated_power;\">\n",
       "            iterated_power: int or &#x27;auto&#x27;, default=&#x27;auto&#x27;<br><br>Number of iterations for the power method computed by<br>svd_solver == &#x27;randomized&#x27;.<br>Must be of range [0, infinity).<br><br>.. versionadded:: 0.18.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_oversamples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_oversamples;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_oversamples,-int%2C%20default%3D10\">\n",
       "            n_oversamples\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_oversamples;\">\n",
       "            n_oversamples: int, default=10<br><br>This parameter is only relevant when `svd_solver=&quot;randomized&quot;`.<br>It corresponds to the additional number of random vectors to sample the<br>range of `X` so as to ensure proper conditioning. See<br>:func:`~sklearn.utils.extmath.randomized_svd` for more details.<br><br>.. versionadded:: 1.1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_iteration_normalizer',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-power_iteration_normalizer;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=power_iteration_normalizer,-%7B%27auto%27%2C%20%27QR%27%2C%20%27LU%27%2C%20%27none%27%7D%2C%20default%3D%27auto%27\">\n",
       "            power_iteration_normalizer\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-power_iteration_normalizer;\">\n",
       "            power_iteration_normalizer: {&#x27;auto&#x27;, &#x27;QR&#x27;, &#x27;LU&#x27;, &#x27;none&#x27;}, default=&#x27;auto&#x27;<br><br>Power iteration normalizer for randomized SVD solver.<br>Not used by ARPACK. See :func:`~sklearn.utils.extmath.randomized_svd`<br>for more details.<br><br>.. versionadded:: 1.1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Used when the &#x27;arpack&#x27; or &#x27;randomized&#x27; solvers are used. Pass an int<br>for reproducible results across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.<br><br>.. versionadded:: 0.18.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-components_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=components_,-ndarray%20of%20shape%20%28n_components%2C%20n_features%29\">\n",
       "            components_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-components_;\">\n",
       "            components_: ndarray of shape (n_components, n_features)<br><br>Principal axes in feature space, representing the directions of<br>maximum variance in the data. Equivalently, the right singular<br>vectors of the centered input data, parallel to its eigenvectors.<br>The components are sorted by decreasing ``explained_variance_``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](123, 165)</td>\n",
       "           <td>[[ 0.  ,-0.04,-0.05,...,-0.04,-0.06,-0.06],\n",
       " [-0.  , 0.03, 0.12,...,-0.03,-0.03,-0.03],\n",
       " [-0.  , 0.17,-0.12,..., 0.03, 0.02, 0.02],\n",
       " ...,\n",
       " [-0.  ,-0.16,-0.08,..., 0.06, 0.02, 0.  ],\n",
       " [-0.  , 0.03,-0.04,...,-0.01,-0.01, 0.  ],\n",
       " [ 0.  , 0.02, 0.07,...,-0.03,-0.01, 0.  ]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-explained_variance_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=explained_variance_,-ndarray%20of%20shape%20%28n_components%2C%29\">\n",
       "            explained_variance_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-explained_variance_;\">\n",
       "            explained_variance_: ndarray of shape (n_components,)<br><br>The amount of variance explained by each of the selected components.<br>The variance estimation uses `n_samples - 1` degrees of freedom.<br><br>Equal to n_components largest eigenvalues<br>of the covariance matrix of X.<br><br>.. versionadded:: 0.18</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](123,)</td>\n",
       "           <td>[42.59,30.71,23.68,..., 0.  , 0.  , 0.  ]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-explained_variance_ratio_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=explained_variance_ratio_,-ndarray%20of%20shape%20%28n_components%2C%29\">\n",
       "            explained_variance_ratio_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-explained_variance_ratio_;\">\n",
       "            explained_variance_ratio_: ndarray of shape (n_components,)<br><br>Percentage of variance explained by each of the selected components.<br><br>If ``n_components`` is not set then all components are stored and the<br>sum of the ratios is equal to 1.0.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](123,)</td>\n",
       "           <td>[0.26,0.19,0.14,...,0.  ,0.  ,0.  ]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-mean_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=mean_,-ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            mean_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-mean_;\">\n",
       "            mean_: ndarray of shape (n_features,)<br><br>Per-feature empirical mean, estimated from the training set.<br><br>Equal to `X.mean(axis=0)`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](165,)</td>\n",
       "           <td>[ 0., 0.,-0.,...,-0., 0.,-0.]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_components_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_components_,-int\">\n",
       "            n_components_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_components_;\">\n",
       "            n_components_: int<br><br>The estimated number of components. When n_components is set<br>to &#x27;mle&#x27; or a number between 0 and 1 (with svd_solver == &#x27;full&#x27;) this<br>number is estimated from input data. Otherwise it equals the parameter<br>n_components, or the lesser value of n_features and n_samples<br>if n_components is None.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>123</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>165</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_samples_,-int\">\n",
       "            n_samples_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_;\">\n",
       "            n_samples_: int<br><br>Number of samples in the training data.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>824</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-noise_variance_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=noise_variance_,-float\">\n",
       "            noise_variance_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-noise_variance_;\">\n",
       "            noise_variance_: float<br><br>The estimated noise covariance following the Probabilistic PCA model<br>from Tipping and Bishop 1999. See &quot;Pattern Recognition and<br>Machine Learning&quot; by C. Bishop, 12.2.1 p. 574 or<br>http://www.miketipping.com/papers/met-mppca.pdf. It is required to<br>compute the estimated data covariance and score samples.<br><br>Equal to the average of (min(n_features, n_samples) - n_components)<br>smallest eigenvalues of the covariance matrix of X.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>1.452e-06</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-singular_values_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.decomposition.PCA.html#:~:text=singular_values_,-ndarray%20of%20shape%20%28n_components%2C%29\">\n",
       "            singular_values_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-singular_values_;\">\n",
       "            singular_values_: ndarray of shape (n_components,)<br><br>The singular values corresponding to each of the selected components.<br>The singular values are equal to the 2-norms of the ``n_components``<br>variables in the lower-dimensional space.<br><br>.. versionadded:: 0.19</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](123,)</td>\n",
       "           <td>[187.21,158.97,139.61,...,  0.09,  0.09,  0.08]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>123 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>pca0</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca7</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca8</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca9</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca10</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca11</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca12</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca13</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca14</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca15</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca16</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca17</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca18</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca19</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca20</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca21</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca22</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca23</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca24</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca25</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca26</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca27</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca28</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca29</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca30</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca31</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca32</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca33</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca34</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca35</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca36</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca37</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca38</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca39</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca40</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca41</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca42</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca43</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca44</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca45</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca46</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca47</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca48</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca49</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca50</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca51</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca52</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca53</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca54</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca55</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca56</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca57</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca58</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca59</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca60</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca61</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca62</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca63</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca64</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca65</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca66</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca67</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca68</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca69</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca70</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca71</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca72</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca73</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca74</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca75</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca76</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca77</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca78</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca79</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca80</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca81</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca82</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca83</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca84</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca85</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca86</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca87</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca88</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca89</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca90</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca91</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca92</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca93</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca94</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca95</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca96</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca97</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca98</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca99</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca100</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca101</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca102</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca103</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca104</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca105</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca106</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca107</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca108</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca109</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca110</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca111</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca112</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca113</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca114</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca115</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca116</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca117</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca118</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca119</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca120</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca121</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>pca122</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___linear__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
       "            copy_X\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
       "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-positive;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
       "            positive\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-positive;\">\n",
       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](123,)</td>\n",
       "           <td>[   0.06,   1.29,   1.35,...,-309.63,-129.24, -33.5 ]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>35.3</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>123</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-rank_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
       "            rank_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-rank_;\">\n",
       "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>123</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-singular_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
       "            singular_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-singular_;\">\n",
       "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](123,)</td>\n",
       "           <td>[187.21,158.97,139.61,...,  0.09,  0.09,  0.08]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[('poly', PolynomialFeatures()),\n",
       "                                       ('scaler', StandardScaler()),\n",
       "                                       ('pca', PCA()),\n",
       "                                       ('linear', LinearRegression())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid=[{'pca__n_components': [4, 5, 6, 7, 8],\n",
       "                          'poly__degree': [1]},\n",
       "                         {'pca__n_components': [15, 16, 17, 18, 19, 20, 21, 22,\n",
       "                                                23, 24, 25, 26, 27, 28, 29, 30,\n",
       "                                                31, 32, 33, 34, 35, 36, 37, 38,\n",
       "                                                39, 40, 41, 42, 43, 44, ...],\n",
       "                          'poly__degree': [2]},\n",
       "                         {'pca__n_components': [105, 107, 109, 111, 113, 115,\n",
       "                                                117, 119, 121, 123, 125, 127,\n",
       "                                                129, 131, 133, 135, 137, 139,\n",
       "                                                141, 143, 145, 147, 149, 151,\n",
       "                                                153, 155, 157, 159, 161, 163, ...],\n",
       "                          'poly__degree': [3]}],\n",
       "             scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Specify the parameter search space as a list of\n",
    "# dictionaries, one for each polynomial degree to test.\n",
    "# This allows us to test different PCA component counts for each degree.\n",
    "\n",
    "params = [\n",
    "    {\n",
    "        'poly__degree': [1], # Degree 1 gives 8 features\n",
    "        'pca__n_components': [4,5,6,7,8],\n",
    "    },\n",
    "    {\n",
    "        'poly__degree': [2], # Degree 2 gives 46 features\n",
    "        'pca__n_components': list(range(15,46)),\n",
    "    },\n",
    "    {\n",
    "        'poly__degree': [3], # Degree 3 gives 165 features\n",
    "        'pca__n_components': list(range(105,166,2)),\n",
    "    }\n",
    "]\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    pipeline,\n",
    "    params,\n",
    "    cv=3,\n",
    "    scoring='neg_root_mean_squared_error',\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8e950e00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pca__n_components: 123\n",
      "poly__degree: 3\n"
     ]
    }
   ],
   "source": [
    "best_params = grid_search.best_params_\n",
    "\n",
    "for key, val in best_params.items():\n",
    "    print(f'{key}: {val}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7351bdad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimized linear pipeline RMSE: 6.51 MPa\n",
      "Optimized linear pipeline R²:   0.858\n"
     ]
    }
   ],
   "source": [
    "winner = grid_search.best_estimator_\n",
    "\n",
    "y_pred_svr = winner.predict(X_test)\n",
    "svr_rmse   = root_mean_squared_error(y_test, y_pred_svr)\n",
    "svr_r2     = r2_score(y_test, y_pred_svr)\n",
    "\n",
    "print(f'Optimized linear pipeline RMSE: {svr_rmse:.2f} MPa')\n",
    "print(f'Optimized linear pipeline R²:   {svr_r2:.3f}')\n",
    "results['Optimized linear pipeline'] = {'rmse': svr_rmse, 'r2': svr_r2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "de07585a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sWYNq1arB1NQUjRo1wowZMxATE5Or4/nQp65T9+7dsz2OXbt2ZXscufn34r///pPeZxdPdstzIzcxERFR4cVH5BIRFRFVq1bFmjVrcOHCBWRkZEBTU/Or7l9D4+vU1fX19ZUul3/77eLiAi8vr4/2kZviy/vfqisb9fAhXV3dHPf9NX3q+uTX9RNCKF3+qevYoUMHGBoafrRv+SgW4N3joTt16oS//voLx44dw7Fjx7BhwwZs2LABwcHBOHr0KGxsbAC8e+Ttvn37cPbsWezZswcnTpzAiRMncO7cOcyfPx/ffvstlixZkqPj69ixI4YMGYIDBw7gwYMHsLOzw4ULF3DlyhXY2tqiadOmSo+vbt26UuEqO+XLl89RDO7u7rhx4wb27t2LgwcP4sSJEzh69CgOHjyIH374Ab/99hu6deuWo74+9Knr5OfnBysrq4/24eDgkGVZXv+85aSY+Clf698wIiJSLRZBiIiKiJYtW2LkyJF4+fIl/vrrrxzdZiEnv31EPupB2WgQ+Te/2d1qkhvyb1yze4zq57C3twfwbnRFeHh4nvVbvHhx6Ovr482bN5g7dy6KFy/+xX3Kj//mzZsQQnzyDzgLCwvo6uoiJSUFd+/eRaVKlbK0ycvrk1MvX77Ey5cvlY4GkT/u1s7OLld92tvb49atWxg3bhyqVauWq22trKzQr18/9OvXD8C7n6/evXvj5MmTGD9+PFatWqXQvnr16qhevTqAd7dcbN++HT169MDSpUvRoUMHNGzY8JP7NDAwQKdOnfDbb79h1apV+P7776Wfv549e2b5w1r+c9qmTRuMHj06V8f3MVpaWmjevLl0W1NiYiLmz5+PqVOnYsCAAfD39/9kUSk37O3t8e+//6JPnz7Z3jKTV97/+b9//z7KlSuXpc2Hj7MmIiLKDkveRERFhLOzMwICAgAAo0aNwvPnzz/aPj4+Hjdu3ADw7g9V+bfSygoIQghpeU7+MPwUHx8f6Ojo4Pz587hw4cIX9we8u59fR0cHkZGRub4lSFtbG8C7P4Q/pKmpiSZNmgAANm3a9OWB4t0cIsWLF8fTp0+xffv2T7bX0tJC3bp1ASi/PgCwcuVKAHlzfXJjzZo1WZalpqZi48aNAP5/HpacatasGYC8Oddubm4YN24cAODSpUsfbaulpYUOHTrA19c3R+3fJ78lZtWqVUhJScH69esBKJ+/RX58mzdvznaUTF4wNjbGlClTYGpqitevX+PmzZvSOh0dHQDKf95zKi+v06doa2ujdu3aACCd2w/9/vvvSpfnxbESEVHRwiIIEVER8tNPP8HFxQX37t1D3bp1cezYsSxtUlNTsXLlSnh6eiIqKkpaLv9Wetq0abh8+bK0XAiB6dOn49KlSzA1NZW+Zf8SJUqUwMCBAwG8u53g2rVrCuuFEDh48CASEhJy3KeVlRWGDBmC5ORktGrVClevXs3SJiUlBX/99VeWESjy0Qr//POP0r6Dg4Oho6ODMWPGYNWqVUonnrx27Rq2bt2ao1i1tLTw/fffAwD69++PI0eOZGlz9uxZhfk0Ro0aBQD4+eefceDAAYW24eHh+Ouvv6CtrY1hw4blKIa8Mm3aNIXrl5mZiXHjxuHBgwewt7dH+/btc9XfmDFjYGpqivnz52PevHlZ5vMAgHv37mHt2rXS+4MHD2LXrl1IS0tTaCeEwI4dOwAo3pKxdOlSqQD4vsePH+PcuXNZ2n9KnTp14OrqKo1gefbsGerWrStNLPu+Nm3aoHr16jhz5gx69eqldL6MFy9eYNmyZTn6w/3169eYP3++0n6OHj2Kly9fQlNTU2FEzqd+3nOif//+cHBwwObNmzFu3Di8evUqS5vHjx9jxYoVn72P9w0dOhQAsHjxYpw6dUph3aJFi3D69Gml21laWkJHRwePHz/+ZGGYiIjUhAqfTENERPngyZMnokGDBtKjSJ2cnESbNm1EQECA8PHxEUZGRgKAMDY2FqdPn5a2y8zMFN27dxcAhJaWlmjUqJEICAgQrq6uAoDQ19cXu3btyrI/+aM07927pzSenj17CgAiLCxMYXlKSopo3bq1ACA0NDRE7dq1RdeuXUXTpk2lx6u+32dOHuuZlpYmunbtKvXp6ekp2rdvLzp37iy8vLyEoaGhACB2796tsN3o0aMFAFG8eHHRqVMn0adPH9GnTx/x33//SW02bdokDAwMpEdpNm3aVAQGBopmzZoJOzs7AUB07tw5+wvzgczMTPHNN99I18nT01N06dJFNG/eXJQuXVoAEIcOHVLYZuLEiQKAkMlkom7duqJr166iSpUqAoDQ1NQUv/32W5b9yB+R+2Ffcp+6fvL43id/7GipUqWEv7+/0NbWFk2aNBFdunSRHrVsaGgojh49mqP+PnT48GFRvHhxAUCUKFFC+Pj4iMDAQNGyZUup/5o1a0rtFyxYIP1MN2jQQHTt2lX4+/tLx2ZiYiIuXrwotffw8JB+N1q1aiUCAwNF06ZNhb6+vgAgfHx8FB5fm90jct8nf4yq/LVy5cps2z58+FBUrlxZOk916tQRXbp0Ee3atROVK1cWmpqaAkCWx+4q8+LFC+nn3cPDQ3To0EEEBASI2rVrC5lMJgCIyZMnK2zz+PFj6XfBy8tLBAUFiT59+ijE/KmfCyGEuHbtmnB0dBQAhKmpqahfv77o2rWraNu2rShXrpyQyWTCyspKYZtP/R5/7Fz3799f+llv0KCBCAgIEBUqVBCamppixIgRAoBo0qRJlu06dOggAAh7e3sREBAg/X7nRUxERFT4sAhCRFRE7d69W/To0UO4uLgIIyMjoa2tLaytrUWTJk3EwoULxbNnz5Rut379etGgQQNhamoqtLW1hb29vQgKChL//vuv0vafWwQR4l0hYP369aJp06bCwsJCirFevXrixx9/VPgjMCdFELldu3aJdu3aCVtbW6GtrS1MTU2Fu7u76NKli1i/fr1ITk5WaP/mzRsxduxY4eLiInR0dKQ/ZD88pnv37okRI0aIChUqCENDQ6GnpyccHBxEgwYNxKxZs8Tt27c/GduHdu/eLdq0aSOsrKyEtra2sLS0FDVq1BBTp05Veo12794tmjdvLiwsLISWlpawtrYWHTt2VChovS8/iyAODg4iLS1NzJgxQ7i5uQldXV1hbm4u2rdvL/75558c96fMkydPxKRJk0SVKlVEsWLFhI6OjrCzsxN16tQRwcHB4sqVK1Lb27dviylTpohGjRqJUqVKCT09PWFmZiYqVaokxo8fL2JjYxX63rFjhxg4cKDw9PQUlpaWUt8NGjQQq1atEqmpqQrtc/JH8KNHj6TihaGhoXj16tVHj+/t27di2bJlomHDhtK1LFGihKhcubIYNGiQiIiI+OQ5EuJd4W/ZsmUiICBAuLm5CRMTE6Gvry+cnZ1F+/btxYEDB5Rud+TIEdG4cWNhZmYmNDQ0BADRs2dPaX1OiiBCCJGYmCjmzJkjateuLf2bYWNjI6pXry7GjBkjTpw4odD+SwoOmZmZYsWKFaJKlSpCT09PmJqaiqZNm4ojR46I1atXCwAiICAgy3bPnj0TAwYMEKVKlRLa2tpZfgZZBCEiUi8yIfLxhlQiIiIqcqKjo+Hk5AQHBwdOSEkFQu/evREWFoZ58+Zh5MiRqg6HiIgKMM4JQkREREQF3j///IPk5GSFZZmZmVixYgXCw8Ohp6cnTQ5NRESUHT4il4iIiIgKvB9//BGbNm2Cp6cnbG1tkZycjOvXryM6OhqamppYunQpbGxsVB0mEREVcCyCEBEREVGB17lzZyQmJuL8+fO4dOkS0tPTUaJECXTu3BnDhw9HrVq1VB0iEREVApwThIiIiIiIiIjUAucEISIiIiIiIiK1wCIIEREREREREakFFkGIiIiIiIiISC2wCEJEREREREREaoFFECIiIiIiIiJSCyyCEBEREREREZFaYBGEiIiIiIiIiNQCiyBEREREREREpBZYBCEiIiIiIiIitcAiCBERERERERGpBRZBiIiIiIiIiEgtsAhCRERERERERGqBRRAiIiIiIiIiUgssghARERERERGRWmARhIiIiIiIiIjUAosgRERERERERKQWWAQhIiIiIiIiIrXAIggRERERERERqQUWQYiIiIiIiIhILbAIQkRERERERERqgUUQIiIiIiIiIlILLIIQERUhkZGRkMlk2LJli6pDyVfy44yMjMz1tuHh4ZDJZIiOjs7zuIiIiAqrBg0aoEKFCqoOI981aNAADRo0+KxtHR0dERQUlKfx0NfHIggR5TuZTJaj1+f8Qfuh169fY8qUKTnuS/7HtPylqamJEiVKoEOHDoiKisrSPigoCDKZDMbGxnjz5k2W9bdu3ZL6mjt3rsK66Oho9OrVC87OztDT04O1tTXq16+P4OBghXYNGjTI9hy5ubnl/GQQERFRrhSVnOXQoUPw8vKCt7c3ypUrh+nTp39xvERFhZaqAyCiom/NmjUK71evXo19+/ZlWe7u7v7F+3r9+jWmTp0KALmq8g8dOhTVq1dHWloarly5gmXLliEyMhLXrl2DtbW1QlstLS28fv0af//9Nzp16qSwbt26ddDT08Pbt28Vlt++fRvVq1eHvr4+evfuDUdHR8TFxeHChQuYPXu2FLOcnZ0dQkJCssRpYmKS42MiIiKi3CkqOUvZsmWxd+9eGBoaIi4uDqVLl0a9evXg7e39xXETFXYsghBRvuvWrZvC+1OnTmHfvn1ZlqtSvXr10KFDB+m9q6srBg4ciNWrV2Ps2LEKbXV1deHl5YXff/89SxFk/fr1aNGiBf744w+F5QsWLEBSUhIuXboEBwcHhXXx8fFZ4jExMSlQ54eIiEgdFJWcxdbWVlovk8mQmZkJDQ3eBEAE8HYYIiogMjMzsXDhQpQvXx56enqwsrLCgAED8OLFC4V2586dg6+vL4oXLw59fX04OTmhd+/eAN7dbmJpaQkAmDp1qjRcdMqUKbmOp169egCAO3fuKF3ftWtX7N69Gy9fvpSWnT17Frdu3ULXrl2ztL9z5w7s7OyyFEAAoESJErmO71MyMjLw3XffwdraGoaGhmjdujViY2Ol9cHBwdDW1sbTp0+zbNu/f3+YmppmGc3yvqCgIBgZGSEmJgYtW7aEkZERbG1tsWTJEgDA1atX4ePjA0NDQzg4OGD9+vVZ+rh79y46duwIc3NzGBgYoFatWti5c2eWdg8ePEDbtm1haGiIEiVKYMSIEUhJSVEa1+nTp+Hn5wcTExMYGBjA29sbx48f/+T5IiIiyqnClLOkp6ejR48eaNGihdTuU86fP486depIMS9btkxal5SUBENDQwwbNizLdg8ePICmpqbSkaxy0dHR0i3DS5YsQenSpWFgYICmTZsiNjYWQghMmzYNdnZ20NfXR5s2bfD8+fMs/SxduhTly5eHrq4uSpYsiUGDBinkZHK//PILnJ2doa+vjxo1auDo0aNK40pJSUFwcDBcXFygq6sLe3t7jB07Ntt8gwo3FkGIqEAYMGAAxowZAy8vLyxatAi9evXCunXr4Ovri7S0NADvRkw0bdoU0dHRGD9+PH766ScEBgbi1KlTAABLS0v8/PPPAAB/f3+sWbMGa9asQbt27XIdj3zSTDMzM6Xr27VrB5lMhq1bt0rL1q9fDzc3N1SpUiVLewcHB8TGxuLgwYM52n9GRgb++++/LK/k5OQcbT9jxgzs3LkT48aNw9ChQ7Fv3z40btxYmseke/fuSE9Px8aNGxW2S01NxZYtW9C+fXvo6el9MsZmzZrB3t4ec+bMgaOjIwYPHozw8HD4+fmhWrVqmD17NooVK4YePXrg3r170rZPnjxBnTp1EBERgW+//RYzZszA27dv0bp1a2zbtk1q9+bNGzRq1AgREREYPHgwvv/+exw9ejTL6BwAOHjwIOrXr4/ExEQEBwdj5syZePnyJXx8fHDmzJkcnTciIqJPKSw5S2ZmJnr16oWkpKQst/Nk58WLF2jevDmqVq2KOXPmwM7ODgMHDsTKlSsBAEZGRvD398fGjRuRkZGhsO3vv/8OIQQCAwM/uZ9169Zh6dKlGDJkCEaNGoXDhw+jU6dOmDhxIvbs2YNx48ahf//++PvvvzF69GiFbadMmYJBgwahZMmSmDdvHtq3b4/ly5ejadOm0vkHgN9++w0DBgyAtbU15syZAy8vryxfCsnPU+vWrTF37ly0atUKP/30E9q2bYsFCxagc+fOOTpvVMgIIqKvbNCgQeL9f36OHj0qAIh169YptNuzZ4/C8m3btgkA4uzZs9n2/fTpUwFABAcH5yiWQ4cOCQBi5cqV4unTp+LRo0diz549wsXFRchkMnHmzBmF9j179hSGhoZCCCE6dOggGjVqJIQQIiMjQ1hbW4upU6eKe/fuCQDixx9/lLa7du2a0NfXFwBE5cqVxbBhw8T27dtFcnJylpi8vb0FAKWvAQMG5Oh4bG1tRWJiorR806ZNAoBYtGiRtKx27dqiZs2aCttv3bpVABCHDh366H569uwpAIiZM2dKy168eCH09fWFTCYTGzZskJb/+++/Wa7J8OHDBQBx9OhRadmrV6+Ek5OTcHR0FBkZGUIIIRYuXCgAiE2bNkntkpOThYuLi0KcmZmZokyZMsLX11dkZmZKbV+/fi2cnJxEkyZNpGVhYWECgLh3795Hj5GIiKiw5iwZGRmie/fuolGjRuLVq1c56l+ef8ybN09alpKSIipXrixKlCghUlNThRBCRERECABi9+7dCttXqlRJeHt7f3Qf8hzJ0tJSvHz5Ulo+YcIEAUB4eHiItLQ0aXlAQIDQ0dERb9++FUIIER8fL3R0dETTpk2lXEEIIUJDQ6VzI4QQqampokSJEqJy5coiJSVFavfLL78IAApxrlmzRmhoaCjkJEIIsWzZMgFAHD9+XFrm4OAgevbs+dFjpIKPI0GISOU2b94MExMTNGnSRGHUQ9WqVWFkZIRDhw4BAExNTQEAO3bsUKj054XevXvD0tISJUuWhJ+fHxISErBmzRpUr1492226du2KyMhIPH78GAcPHsTjx4+V3goDAOXLl8elS5fQrVs3REdHY9GiRWjbti2srKywYsWKLO0dHR2xb9++LK/hw4fn6Hh69OiBYsWKSe87dOgAGxsb7Nq1S6HN6dOnFYbPrlu3Dvb29jmeOK1v377S/5uamsLV1RWGhoYKc6W4urrC1NQUd+/elZbt2rULNWrUQN26daVlRkZG6N+/P6Kjo3H9+nWpnY2NjcK9zwYGBujfv79CHJcuXZJuRXr27JnCyJlGjRrhyJEjyMzMzNExERERZaew5Cy//fYb1qxZg9evX6Nly5Zo0KCBwkjL7GhpaWHAgAHSex0dHQwYMADx8fE4f/48AKBx48YoWbIk1q1bJ7W7du0arly5kuO5Uzp27Kgw2XvNmjUBvJuTRUtLS2F5amoqHj58CADYv38/UlNTMXz4cIU5Tvr16wdjY2Ppttpz584hPj4e33zzDXR0dKR2QUFBWSaZ37x5M9zd3eHm5qZwTX18fABAuqZUdHBiVCJSuVu3biEhISHbuTHkE4d6e3ujffv2mDp1KhYsWIAGDRqgbdu26Nq1K3R1db8ohsmTJ6NevXpISkrCtm3bsGHDhk9OINa8eXMUK1YMGzduxKVLl1C9enW4uLhIw1I/VLZsWaxZswYZGRm4fv06duzYgTlz5qB///5wcnJC48aNpbaGhoYK73OrTJkyCu9lMlmW2Dp37ozhw4dj3bp1mDx5MhISErBjxw6MGDECMpnsk/vQ09OT7meWMzExgZ2dXZbtTUxMFO6Vvn//vpTwvE8+2/79+/dRoUIF3L9/Hy4uLln6c3V1VXh/69YtAEDPnj2zjTchISHb25uIiIhyorDkLP369UO/fv1y3XfJkiVhaGiosKxs2bIA3t12U6tWLWhoaCAwMBA///wzXr9+DQMDA+npeB07dszRfkqVKqXwXl6YsLe3V7pcnkPcv38fQNY8QEdHB6VLl5bWy//7YT6kra2N0qVLKyy7desWoqKisuQ0csomsKfCjUUQIlK5zMxMlChRQuEbhffJP5RkMhm2bNmCU6dO4e+//0ZERAR69+6NefPm4dSpUzAyMvrsGCpWrCgVHdq2bYvXr1+jX79+qFu3bpYPZDldXV20a9cOq1atwt27d3M8mZmmpiYqVqyIihUronbt2mjYsCHWrVv3RUWPz2FmZoaWLVtKRZAtW7YgJSUlx9/iaGpq5mq5EOKzY/0U+SiPH3/8EZUrV1ba5kt+PoiIiIDCm7PktR49euDHH3/E9u3bERAQgPXr16Nly5ZZRllkp6DlEBUrVsT8+fOVrv9a55S+HhZBiEjlnJ2dsX//fnh5eUFfX/+T7WvVqoVatWphxowZWL9+PQIDA7Fhwwb07ds3RyMYcmLWrFnYtm0bZsyYoTAr+oe6du2KlStXQkNDA126dMn1fqpVqwYAiIuL++xYlZGPjJATQuD27duoVKmSwvIePXqgTZs2OHv2LNatWwdPT0+UL18+T2NRxsHBATdu3Miy/N9//5XWy/977do1CCEUru2H2zo7OwMAjI2Nv3oxiYiI1Edhzlly4tGjR0hOTlYYDXLz5k0A727VlatQoQI8PT2xbt062NnZISYmBj/99NMX7Tsn5PnBjRs3FEZ0pKam4t69e1IOIG9369Yt6bYWAEhLS8O9e/fg4eEhLXN2dsbly5fRqFGjPLsmVLBxThAiUrlOnTohIyMD06ZNy7IuPT1deuTZixcvsnwTIP/WX/4IMwMDAwBQ+pi03HB2dkb79u0RHh6Ox48fZ9uuYcOGmDZtGkJDQ2FtbZ1tu6NHjyq9J1g+R8eHwzq/1OrVq/Hq1Svp/ZYtWxAXF4dmzZoptGvWrBmKFy+O2bNn4/DhwzkeBfKlmjdvjjNnzuDkyZPSsuTkZPzyyy9wdHREuXLlpHaPHj3Cli1bpHavX7/GL7/8otBf1apV4ezsjLlz5yIpKSnL/pQ9CpiIiCi3CnPOkhPp6elYvny59D41NRXLly+HpaUlqlatqtC2e/fu2Lt3LxYuXAgLC4ssOUZ+aNy4MXR0dLB48WKF8/vbb78hISEBLVq0APDuSyZLS0ssW7YMqampUrvw8PAs57tTp054+PCh0jna3rx5k+Mn81HhwZEgRKRy3t7eGDBgAEJCQnDp0iU0bdoU2trauHXrFjZv3oxFixahQ4cOWLVqFZYuXQp/f384Ozvj1atXWLFiBYyNjdG8eXMAgL6+PsqVK4eNGzeibNmyMDc3R4UKFVChQoVcxzVmzBhs2rQJCxcuxKxZs5S20dDQwMSJEz/Z1+zZs3H+/Hm0a9dOGo1x4cIFrF69Gubm5lkmPE1ISMDatWuV9pWTQoW5uTnq1q2LXr164cmTJ1i4cCFcXFyy3B+sra2NLl26IDQ0FJqamggICPhk33lh/Pjx+P3339GsWTMMHToU5ubmWLVqFe7du4c//vhDure5X79+CA0NRY8ePXD+/HnY2NhgzZo1UuIop6GhgV9//RXNmjVD+fLl0atXL9ja2uLhw4c4dOgQjI2N8ffff3+VYyMioqKrMOcsOVGyZEnMnj0b0dHRKFu2rDTv2S+//AJtbW2Ftl27dsXYsWOxbds2DBw4MMv6/GBpaYkJEyZg6tSp8PPzQ+vWrXHjxg0sXboU1atXl3IkbW1tTJ8+HQMGDICPjw86d+6Me/fuISwsLMucIN27d8emTZvwzTff4NChQ/Dy8kJGRgb+/fdfbNq0CREREdLIXSoiVPhkGiJSUx8+bk7ul19+EVWrVhX6+vqiWLFiomLFimLs2LHi0aNHQgghLly4IAICAkSpUqWErq6uKFGihGjZsqU4d+6cQj8nTpwQVatWFTo6Op989Jz8cXObN29Wur5BgwbC2NhYeozb+4/IzY6yR+QeP35cDBo0SFSoUEGYmJgIbW1tUapUKREUFCTu3LmjsP3HHpH7qX+25cfz+++/iwkTJogSJUoIfX190aJFC3H//n2l25w5c0YAEE2bNv1o3+/L7jx4e3uL8uXLZ1nu4OAgWrRoobDszp07okOHDsLU1FTo6emJGjVqiB07dmTZ9v79+6J169bCwMBAFC9eXAwbNkx6FOGHj/K9ePGiaNeunbCwsBC6urrCwcFBdOrUSRw4cEBqw0fkEhFRThXmnCW35J/h586dE7Vr1xZ6enrCwcFBhIaGZrtN8+bNBQBx4sSJHO1DWY4kRPbHJv/M/vBRw6GhocLNzU1oa2sLKysrMXDgQPHixYss+1u6dKlwcnISurq6olq1auLIkSPC29s7y6N8U1NTxezZs0X58uWFrq6uMDMzE1WrVhVTp04VCQkJUjs+IrdokAmRj7PMEBFRgXf58mVUrlwZq1evRvfu3VUdDhERERUS/v7+uHr1Km7fvq3qUIhyjHOCEBGpuRUrVsDIyAjt2rVTdShERERUSMTFxWHnzp38AoUKHc4JQkSkpv7++29cv34dv/zyCwYPHqwwEzwRERGRMvfu3cPx48fx66+/QltbGwMGDFB1SES5wiIIEZGaGjJkCJ48eYLmzZtj6tSpqg6HiIiICoHDhw+jV69eKFWqFFatWvXRp+MRFUScE4SIiIiIiIiI1ALnBCEiIiIiIiIitcDbYUjtZWZm4tGjRyhWrBhkMpmqwyEiKjKEEHj16hVKliwJDQ1+70Ikx9yDiCjv5TTvYBGE1N6jR49gb2+v6jCIiIqs2NhY2NnZqToMogKDuQcRUf75VN7BIgipvWLFigF498tibGys4miIiIqOxMRE2NvbS//OEtE78t+J69evw9bWVsXREBEVDTnNO1gEIbUnH4ZqbGzMIggRUT7gcH8iRfLfiWLFijH3ICLKY5/KO3iDLhERERERERGpBRZBiIiIiIiIiEgtsAhCRERERERERGqBRRAiIiIiIiIiUgssghARERERERGRWmARhIiIiIiIiIjUAosgRERERERERKQWWAQhIiIiIiIiIrXAIggRERERERERqQUWQYiIiIiIiIhILWipOgCigsJ/dgS09AxUHQYRUYERMamFqkMgKtLs588HTExUHQZRoSSCg1UdAhVSHAlCRERERERERGqBRRAiIiIiIiIiUgssghARERERERGRWmARhIiIiIiIiIjUAosgRERERERERKQWWATJIZlMhu3bt6s6DLXAc01EROqOn4VfD881EZF64SNy/ycoKAgvX77M9kMwLi4OZmZmXzcoNcVzTURERR3zjoKD55qISL2wCJJD1tbWqg4BQghkZGRAS+vTly0tLQ3a2toqjeFzFYRzTUREpEoF4bOQeQcRERVFvB0mh94fKhkdHQ2ZTIatW7eiYcOGMDAwgIeHB06ePKmwzbFjx1CvXj3o6+vD3t4eQ4cORXJysrR+zZo1qFatGooVKwZra2t07doV8fHx0vrIyEjIZDLs3r0bVatWha6uLo4dO5YlNnk8GzduhLe3N/T09LBu3ToAwK+//gp3d3fo6enBzc0NS5cuVdj2xIkTqFy5MvT09FCtWjVs374dMpkMly5d+mgMmZmZCAkJgZOTE/T19eHh4YEtW7ZI/b548QKBgYGwtLSEvr4+ypQpg7CwMABAamoqBg8eDBsbG+jp6cHBwQEhISFKzzUAXL16FT4+PtDX14eFhQX69++PpKQkaX1QUBDatm2LuXPnwsbGBhYWFhg0aBDS0tJycmmJiIgKHOYdzDuIiCh/cCTIF/j+++8xd+5clClTBt9//z0CAgJw+/ZtaGlp4c6dO/Dz88P06dOxcuVKPH36FIMHD8bgwYOlD+W0tDRMmzYNrq6uiI+Px8iRIxEUFIRdu3Yp7Gf8+PGYO3cuSpcu/dHhmuPHj8e8efPg6ekpJSSTJ09GaGgoPD09cfHiRfTr1w+Ghobo2bMnEhMT0apVKzRv3hzr16/H/fv3MXz48Gz7fj+GkJAQrF27FsuWLUOZMmVw5MgRdOvWDZaWlvD29sakSZNw/fp17N69G8WLF8ft27fx5s0bAMDixYvx119/YdOmTShVqhRiY2MRGxurdL/Jycnw9fVF7dq1cfbsWcTHx6Nv374YPHgwwsPDpXaHDh2CjY0NDh06hNu3b6Nz586oXLky+vXrl6XPlJQUpKSkSO8TExOzPadEREQFBfOOwpl3AMw9iIgKEhZBvsDo0aPRokULAMDUqVNRvnx53L59G25ubggJCUFgYKD04V6mTBksXrwY3t7e+Pnnn6Gnp4fevXtLfZUuXRqLFy9G9erVkZSUBCMjI2ndDz/8gCZNmnwynuHDh6Ndu3bS++DgYMybN09a5uTkhOvXr2P58uXo2bMn1q9fD5lMhhUrVkBPTw/lypXDw4cPlX6Avx9DSkoKZs6cif3796N27dpS/MeOHcPy5cvh7e2NmJgYeHp6olq1agAAR0dHqa+YmBiUKVMGdevWhUwmg4ODQ7bHtH79erx9+xarV6+GoaEhACA0NBStWrXC7NmzYWVlBQAwMzNDaGgoNDU14ebmhhYtWuDAgQNKjyUkJARTp0795PkkIiIqSJh3FM68A2DuQURUkPB2mC9QqVIl6f9tbGwAQBpWevnyZYSHh8PIyEh6+fr6IjMzE/fu3QMAnD9/Hq1atUKpUqVQrFgxeHt7A3j3Yf0++Qf6p7zfLjk5GXfu3EGfPn0UYpg+fTru3LkDALhx4wYqVaoEPT09absaNWp8su/bt2/j9evXaNKkiULfq1evlvoeOHAgNmzYgMqVK2Ps2LE4ceKEtH1QUBAuXboEV1dXDB06FHv37s32mKKiouDh4SElIgDg5eWFzMxM3LhxQ1pWvnx5aGpqSu9tbGwUhvi+b8KECUhISJBe2X0bREREVJAw7yiceQfA3IOIqCDhSJAv8P4EYDKZDACQmZkJAEhKSsKAAQMwdOjQLNuVKlVKGm7p6+uLdevWwdLSEjExMfD19UVqaqpC+/c/iD/m/Xbye1dXrFiBmjVrKrR7/0M7p5T1vXPnTtja2iq009XVBQA0a9YM9+/fx65du7Bv3z40atQIgwYNwty5c1GlShXcu3cPu3fvxv79+9GpUyc0btxY4d7e3PpwMjaZTCZdiw/p6upKcRIRERUWzDsKZ94hj5O5BxFRwcAiSD6pUqUKrl+/DhcXF6Xrr169imfPnmHWrFmwt7cHAJw7dy7P9m9lZYWSJUvi7t27CAwMVNrG1dUVa9euRUpKivTBfPbs2U/2Xa5cOejq6iImJkb6FkkZS0tL9OzZEz179kS9evUwZswYzJ07FwBgbGyMzp07o3PnzujQoQP8/Pzw/PlzmJubK/Th7u6O8PBwJCcnSwnR8ePHoaGhAVdX1xydCyIioqKOeQfzDiIiyhkWQd6TkJAgzU4uZ2FhISULuTFu3DjUqlULgwcPRt++fWFoaIjr169j3759CA0NRalSpaCjo4OffvoJ33zzDa5du4Zp06bl0ZG8M3XqVAwdOhQmJibw8/NDSkoKzp07hxcvXmDkyJHo2rUrvv/+e/Tv3x/jx49HTEyMlCzIv2FSplixYhg9ejRGjBiBzMxM1K1bFwkJCTh+/DiMjY3Rs2dPTJ48GVWrVkX58uWRkpKCHTt2wN3dHQAwf/582NjYwNPTExoaGti8eTOsra1hamqaZV+BgYEIDg5Gz549MWXKFDx9+hRDhgxB9+7dpftyiYiICiPmHcw7iIjo62MR5D2RkZHw9PRUWNanTx/8+uuvue6rUqVKOHz4ML7//nvUq1cPQgg4Ozujc+fOAN59WxEeHo7vvvsOixcvRpUqVTB37ly0bt06T44FAPr27QsDAwP8+OOPGDNmDAwNDVGxYkVp0jRjY2P8/fffGDhwICpXroyKFSti8uTJ6Nq1q8L9uspMmzYNlpaWCAkJwd27d2FqaooqVargu+++AwDo6OhgwoQJiI6Ohr6+PurVq4cNGzYAeJfMzJkzB7du3YKmpiaqV6+OXbt2QUMj6xQ1BgYGiIiIwLBhw1C9enUYGBigffv2mD9/fp6dJyIiIlVg3sG8g4iIvj6ZEEKoOggqONatW4devXohISEB+vr6qg7nq0hMTISJiQl8vtsELT0DVYdDRFRgRExq8UXby/99TUhIgLGxcR5FRUWJOuYdwP//bmDECMDERNXhEBVKIjhY1SFQAZPTvIMjQdTc6tWrUbp0adja2uLy5csYN24cOnXqpFaJCBEREX0dzDuIiEjVWARRc48fP8bkyZPx+PFj2NjYoGPHjpgxY4aqwyIiIqIiiHkHERGpGosgam7s2LEYO3asqsMgIiIiNcC8g4iIVC3rjFBEREREREREREUQR4IQ/c+2cb6cuI+IiIi+mtiRI2FnZ6fqMIiI1ApHghARERERERGRWmARhIiIiIiIiIjUAosgRERERERERKQWWAQhIiIiIiIiIrXAiVGJ/sd/dgS09AxUHQYRqUDEpBaqDoGI1JD9/PmAiYmqwyCiAkQEB6s6hCKPI0GIiIiIiIiISC2wCEJEREREREREaoFFECIiIiIiIiJSCyyCEBEREREREZFaYBGEiIiIiIiIiNQCiyBEREREREREpBZYBFEDjo6OWLhwYZ63JSIiIlKGuQcRERVULIKoSFBQEGQyGWQyGbS1tWFlZYUmTZpg5cqVyMzMzNN9nT17Fv3798/ztp8rPDwcpqam+boPIiIiUsTcwzRf90FERIUDiyAq5Ofnh7i4OERHR2P37t1o2LAhhg0bhpYtWyI9PT3P9mNpaQkDA4M8b5vfUlNTVR0CERFRkcLc4+OYexARFX0sgqiQrq4urK2tYWtriypVquC7777Dn3/+id27dyM8PFxq9/LlS/Tt2xeWlpYwNjaGj48PLl++rNDX33//jerVq0NPTw/FixeHv7+/tO79YaZCCEyZMgWlSpWCrq4uSpYsiaFDhyptCwAxMTFo06YNjIyMYGxsjE6dOuHJkyfS+ilTpqBy5cpYs2YNHB0dYWJigi5duuDVq1dKjzkyMhK9evVCQkKC9G3UlClTpH1PmzYNPXr0gLGxsfSt0LFjx1CvXj3o6+vD3t4eQ4cORXJystRnSkoKRo8eDVtbWxgaGqJmzZqIjIzMzaUgIiJSC8w9mHsQEak7FkEKGB8fH3h4eGDr1q3Sso4dOyI+Ph67d+/G+fPnUaVKFTRq1AjPnz8HAOzcuRP+/v5o3rw5Ll68iAMHDqBGjRpK+//jjz+wYMECLF++HLdu3cL27dtRsWJFpW0zMzPRpk0bPH/+HIcPH8a+fftw9+5ddO7cWaHdnTt3sH37duzYsQM7duzA4cOHMWvWLKV91qlTBwsXLoSxsTHi4uIQFxeH0aNHS+vnzp0LDw8PXLx4EZMmTcKdO3fg5+eH9u3b48qVK9i4cSOOHTuGwYMHS9sMHjwYJ0+exIYNG3DlyhV07NgRfn5+uHXrltIYUlJSkJiYqPAiIiJSV8w9mHsQEakTLVUHQFm5ubnhypUrAN59E3HmzBnEx8dDV1cXwLsP6+3bt2PLli3o378/ZsyYgS5dumDq1KlSHx4eHkr7jomJgbW1NRo3bgxtbW2UKlUq26TlwIEDuHr1Ku7duwd7e3sAwOrVq1G+fHmcPXsW1atXB/AuYQkPD0exYsUAAN27d8eBAwcwY8aMLH3q6OjAxMQEMpkM1tbWWdb7+Phg1KhR0vu+ffsiMDAQw4cPBwCUKVMGixcvhre3N37++WfEx8cjLCwMMTExKFmyJABg9OjR2LNnD8LCwjBz5sws+wgJCVE4V0REROqOuQdzDyIidcGRIAWQEAIymQwAcPnyZSQlJcHCwgJGRkbS6969e7hz5w4A4NKlS2jUqFGO+u7YsSPevHmD0qVLo1+/fti2bVu29wBHRUXB3t5eSkIAoFy5cjA1NUVUVJS0zNHRUUpCAMDGxgbx8fG5Pm4AqFatmsL7y5cvIzw8XOHYfX19kZmZiXv37uHq1avIyMhA2bJlFdocPnxYOj8fmjBhAhISEqRXbGzsZ8VKRERUVDD3+H/MPYiIijaOBCmAoqKi4OTkBABISkqCjY2N0vtM5bOc6+vr57hve3t73LhxA/v378e+ffvw7bff4scff8Thw4ehra39WfF+uJ1MJvvsWeYNDQ0V3iclJWHAgAEK9w7LlSpVCleuXIGmpibOnz8PTU1NhfVGRkZK96Grqyt9s0VERETMPd7H3IOIqGhjEaSAOXjwIK5evYoRI0YAAKpUqYLHjx9DS0sLjo6OSrepVKkSDhw4gF69euVoH/r6+mjVqhVatWqFQYMGwc3NDVevXkWVKlUU2rm7uyM2NhaxsbHSNzLXr1/Hy5cvUa5cuc8+Rh0dHWRkZOSobZUqVXD9+nW4uLgoXe/p6YmMjAzEx8ejXr16nx0TERGRumLuoYi5BxFR0cYiiAqlpKTg8ePHyMjIwJMnT7Bnzx6EhISgZcuW6NGjBwCgcePGqF27Ntq2bYs5c+agbNmyePTokTQhWbVq1RAcHIxGjRrB2dkZXbp0QXp6Onbt2oVx48Zl2Wd4eDgyMjJQs2ZNGBgYYO3atdDX14eDg0OWto0bN0bFihURGBiIhQsXIj09Hd9++y28vb2zDB3NDUdHRyQlJeHAgQPw8PCAgYFBto/GGzduHGrVqoXBgwejb9++MDQ0xPXr17Fv3z6EhoaibNmyCAwMRI8ePTBv3jx4enri6dOnOHDgACpVqoQWLVp8dpxERERFDXMP5h5EROqOc4Ko0J49e2BjYwNHR0f4+fnh0KFDWLx4Mf78809peKVMJsOuXbtQv3599OrVC2XLlkWXLl1w//59WFlZAQAaNGiAzZs346+//kLlypXh4+ODM2fOKN2nqakpVqxYAS8vL1SqVAn79+/H33//DQsLiyxtZTIZ/vzzT5iZmaF+/fpo3LgxSpcujY0bN37RcdepUwfffPMNOnfuDEtLS8yZMyfbtpUqVcLhw4dx8+ZN1KtXD56enpg8ebI0ERkAhIWFoUePHhg1ahRcXV3Rtm1bnD17FqVKlfqiOImIiIoa5h7MPYiI1J1MCCFUHQSRKiUmJsLExAQ+322Clp7yb4WIqGiLmMRvbvOD/N/XhIQEGBsbqzocogJD/ruBESMAExNVh0NEBYgIDlZ1CIVWTvMOjgQhIiIiIiIiIrXAIggRERERERERqQUWQYiIiIiIiIhILbAIQkRERERERERqgY/IJfqfbeN8OXEfERERfTWxI0fCzs5O1WEQEakVjgQhIiIiIiIiIrXAIggRERERERERqQUWQYiIiIiIiIhILbAIQkRERERERERqgROjEv2P/+wIaOkZqDoMIspGxKQWqg6BiChP2c+fD5iYqDoMIioiRHCwqkMoFDgShIiIiIiIiIjUAosgRERERERERKQWWAQhIiIiIiIiIrXAIggRERERERERqQUWQYiIiIiIiIhILbAIQkRERERERERqgUUQIiIiIiIiIlILLILQVxUUFIS2bdsqLNuyZQv09PQwb948BAUFQSaTYdasWQpttm/fDplMJr2PjIyETCZD+fLlkZGRodDW1NQU4eHh+XUIREREVIgw9yAiovexCEIq9euvvyIwMBA///wzRo0aBQDQ09PD7Nmz8eLFi09uf/fuXaxevTq/wyQiIqIigrkHEZF6YxGEVGbOnDkYMmQINmzYgF69eknLGzduDGtra4SEhHyyjyFDhiA4OBgpKSk53m9KSgoSExMVXkRERFT0MfcgIiIWQUglxo0bh2nTpmHHjh3w9/dXWKepqYmZM2fip59+woMHDz7az/Dhw5Geno6ffvopx/sOCQmBiYmJ9LK3t/+sYyAiIqLCg7kHEREBLIKQCuzevRtz5szBn3/+iUaNGilt4+/vj8qVKyM4OPijfRkYGCA4OBghISFISEjI0f4nTJiAhIQE6RUbG5vrYyAiIqLCg7kHERHJsQhCX12lSpXg6OiI4OBgJCUlZdtu9uzZWLVqFaKioj7aX58+fWBhYYHZs2fnaP+6urowNjZWeBEREVHRxdyDiIjkWAShr87W1haRkZF4+PAh/Pz88OrVK6Xt6tevD19fX0yYMOGj/WlpaWHGjBlYtGgRHj16lB8hExERUSHG3IOIiORYBCGVcHBwwOHDh/H48eOPJiOzZs3C33//jZMnT360v44dO6J8+fKYOnVqfoRLREREhRxzDyIiAlgEIRWyt7dHZGQk4uPj4evrq3Sm9IoVKyIwMBCLFy/+ZH+zZs3CypUrkZycnB/hEhERUSHH3IOIiFgEIZWys7NDZGQk/vvvv2yTkR9++AGZmZmf7MvHxwc+Pj5IT0/Pj1CJiIioCGDuQUSk3mRCCKHqIIhUKTExESYmJvD5bhO09AxUHQ4RZSNiUgtVh0C5JP/3NSEhgRNBEr1H/ruBESMAExNVh0NERYT4xNOtirqc5h0cCUJEREREREREaoFFECIiIiIiIiJSCyyCEBEREREREZFaYBGEiIiIiIiIiNSClqoDICooto3z5cR9RERE9NXEjhwJOzs7VYdBRKRWOBKEiIiIiIiIiNQCiyBEREREREREpBZYBCEiIiIiIiIitcAiCBERERERERGpBU6MSvQ//rMjoKVnoOowiAqFiEktVB0CEVGhZz9/PmBiouowiIgkIjhY1SHkO44EISIiIiIiIiK1wCIIEREREREREakFFkGIiIiIiIiISC2wCEJEREREREREaoFFECIiIiIiIiJSCyyCEBEREREREZFaKPRFkClTpqBy5cpf1Ed0dDRkMhkuXbqUJzEpEx4eDlNT03zr/2uRyWTYvn17nvbZoEEDDB8+XHrv6OiIhQsX5uk+iIiI8gLzjq+LeQcREeW1fC+CxMbGonfv3ihZsiR0dHTg4OCAYcOG4dmzZ7nuS9kH4ejRo3HgwIEvitHe3h5xcXGoUKHCF/XzpQ4fPgwfHx+Ym5vDwMAAZcqUQc+ePZGamoo//vgDmpqaePjwodJty5Qpg5EjRwJ49+Euk8kgk8mgp6eHsmXLIiQkBEKIL44xLi4OzZo1++J+Pubs2bPo379/vu6DiIiKJuYdOce84x3mHURE6iVfiyB3795FtWrVcOvWLfz++++4ffs2li1bhgMHDqB27dp4/vz5F+/DyMgIFhYWX9SHpqYmrK2toaWl9cXxfK7r16/Dz88P1apVw5EjR3D16lX89NNP0NHRQUZGBlq3bg0LCwusWrUqy7ZHjhzB7du30adPH2lZv379EBcXhxs3bmDChAmYPHkyli1b9sVxWltbQ1dX94v7+RhLS0sYGBjk6z6IiKjoYd6Rc8w7/h/zDiIi9ZKvRZBBgwZBR0cHe/fuhbe3N0qVKoVmzZph//79ePjwIb7//nupraOjI6ZNm4aAgAAYGhrC1tYWS5YsUVgPAP7+/pDJZNL7D4elBgUFoW3btpg5cyasrKxgamqKH374Aenp6RgzZgzMzc1hZ2eHsLAwaZsPh6UGBQVJ32i8/4qMjAQApKSkYPTo0bC1tYWhoSFq1qwprZMLDw9HqVKlYGBgAH9//09+A7V3715YW1tjzpw5qFChApydneHn54cVK1ZAX18f2tra6N69O8LDw7Nsu3LlStSsWRPly5eXlhkYGMDa2hoODg7o1asXKlWqhH379n00hk9dA0DxWzH5eduwYQPq1KkDPT09VKhQAYcPH1bY5tq1a2jWrBmMjIxgZWWF7t2747///vtoHO8PS5XJZPj111/h7+8vfVP1119/ffY+UlJSkJiYqPAiIqLCj3kH8w6g4OUdAHMPIqKCJN+KIM+fP0dERAS+/fZb6OvrK6yztrZGYGAgNm7cqDBU8scff4SHhwcuXryI8ePHY9iwYdIH6NmzZwEAYWFhiIuLk94rc/DgQTx69AhHjhzB/PnzERwcjJYtW8LMzAynT5/GN998gwEDBuDBgwdKt1+0aBHi4uKk17Bhw1CiRAm4ubkBAAYPHoyTJ09iw4YNuHLlCjp27Ag/Pz/cunULAHD69Gn06dMHgwcPxqVLl9CwYUNMnz79o+fL2toacXFxOHLkSLZt+vTpg1u3bim0SUpKwpYtWxS+jXmfEAJHjx7Fv//+Cx0dnY/GAHz8GmRnzJgxGDVqFC5evIjatWujVatWUvL18uVL+Pj4wNPTE+fOncOePXvw5MkTdOrU6ZOxvG/q1Kno1KkTrly5gubNmyMwMFD6Ri+3+wgJCYGJiYn0sre3z1UsRERU8DDvYN4BFMy8A2DuQURUkORbEeTWrVsQQsDd3V3pend3d7x48QJPnz6Vlnl5eWH8+PEoW7YshgwZgg4dOmDBggUA3g1VBABTU1NYW1tL75UxNzfH4sWL4erqit69e8PV1RWvX7/Gd999hzJlymDChAnQ0dHBsWPHlG5vYmICa2trWFtb48SJE1i+fDm2bt0Ka2trxMTEICwsDJs3b0a9evXg7OyM0aNHo27dutK3PIsWLYKfnx/Gjh2LsmXLYujQofD19f3o+erYsSMCAgLg7e0NGxsb+Pv7IzQ0VOGbgnLlyqFWrVpYuXKltGzTpk0QQqBLly4K/S1duhRGRkbQ1dVF/fr1kZmZiaFDh340BuDj1yA7gwcPRvv27eHu7o6ff/4ZJiYm+O233wAAoaGh8PT0xMyZM+Hm5gZPT0+sXLkShw4dws2bNz8Zj1xQUBACAgLg4uKCmTNnIikpCWfOnPmsfUyYMAEJCQnSKzY2NsdxEBFRwcS8g3kHUDDzDoC5BxFRQZLvE6PmZlKs2rVrZ3kfFRWV632WL18eGhr/f2hWVlaoWLGi9F5TUxMWFhaIj4//aD8XL15E9+7dERoaCi8vLwDA1atXkZGRgbJly8LIyEh6HT58GHfu3AEAREVFoWbNmh89tg9pamoiLCwMDx48wJw5c2Bra4uZM2eifPnyiIuLk9r17t0bW7ZswatXrwC8G5LasWNHFCtWTKG/wMBAXLp0CcePH0ezZs3w/fffo06dOh+NQVmcObkG72+jpaWFatWqSdtcvnwZhw4dUjhX8m+25OcrJypVqiT9v6GhIYyNjaXrl9t96OrqwtjYWOFFRERFA/MO5cf2IeYdH5eXeQfA3IOIqCDJtxm5XFxcIJPJEBUVBX9//yzro6KiYGZm9tFvVj6Xtra2wnuZTKZ0WWZmZrZ9PH78GK1bt0bfvn0VhnwmJSVBU1MT58+fh6ampsI2RkZGXxy7ra0tunfvju7du2PatGkoW7Ysli1bhqlTpwIAunTpghEjRmDTpk2oX78+jh8/jpCQkCz9mJiYwMXFBcC7b21cXFxQq1YtNG7c+ItjzI2kpCS0atUKs2fPzrLOxsYmx/187Prl1T6IiKjwYt7xeZh3KMe8g4io6Mq3IoiFhQWaNGmCpUuXYsSIEQr35z5+/Bjr1q1Djx49IJPJpOWnTp1S6OPUqVMKw1q1tbWRkZGRXyFL3r59izZt2sDNzQ3z589XWOfp6YmMjAzEx8ejXr16Srd3d3fH6dOnFZZ9eGw5YWZmBhsbGyQnJ0vLihUrho4dO2LlypW4c+cOypYtm20cckZGRhg2bBhGjx6NixcvKpzzD33qGmS3Tf369QEA6enpOH/+PAYPHgwAqFKlCv744w84Ojrm2yz4X2MfRERUsDHvYN4BMO8gIqJPy9fbYUJDQ5GSkgJfX18cOXIEsbGx2LNnD5o0aQJbW1vMmDFDof3x48cxZ84c3Lx5E0uWLMHmzZsxbNgwab2joyMOHDiAx48f48WLF/kW94ABAxAbG4vFixfj6dOnePz4MR4/fozU1FSULVsWgYGB6NGjB7Zu3Yp79+7hzJkzCAkJwc6dOwEAQ4cOxZ49ezB37lzcunULoaGh2LNnz0f3uXz5cgwcOBB79+7FnTt38M8//2DcuHH4559/0KpVK4W2ffr0wYkTJ7Bs2TL07t07x8d08+ZN/PHHHx9t96lroMySJUuwbds2/Pvvvxg0aBBevHghxTVo0CA8f/4cAQEBOHv2LO7cuYOIiAj06tUrzxLLr7EPIiIq+Jh3MO9g3kFERJ+Sr0WQMmXK4Ny5cyhdujQ6deoEZ2dn9O/fHw0bNsTJkydhbm6u0H7UqFE4d+4cPD09MX36dMyfP19hYq958+Zh3759sLe3h6enZ77FffjwYcTFxaFcuXKwsbGRXidOnADwbqb4Hj16YNSoUXB1dUXbtm1x9uxZlCpVCgBQq1YtrFixAosWLYKHhwf27t2LiRMnfnSfNWrUQFJSEr755huUL18e3t7eOHXqFLZv3w5vb2+FtnXr1oWrqysSExPRo0ePHB2Tubk5evTogSlTpnx0OO6nroEys2bNwqxZs+Dh4YFjx47hr7/+QvHixQEAJUuWxPHjx5GRkYGmTZuiYsWKGD58OExNTRXun/4SX2MfRERU8DHvYN7BvIOIiD5FJnIzg1g+cnR0xPDhwzF8+HBVh6K2cnsNoqOj4eTkhIsXL6Jy5cr5Glt+SkxMhImJCXy+2wQtPQNVh0NUKERMaqHqEKgQkP/7mpCQUOAmgmTeoXrqmncA//+7gREjABMTVYdDRCQRwcGqDuGz5TTvYLmaiIiIiIiIiNQCiyBEREREREREpBYKzJTW0dHRqg5B7eX2Gjg6OqKA3E1FRESUK8w7VI95BxERqUKBKYIQqdq2cb4F7p51IiIiKrpiR46EnZ2dqsMgIlIrvB2GiIiIiIiIiNQCiyBEREREREREpBZYBCEiIiIiIiIitcAiCBERERERERGpBRZBiIiIiIiIiEgt8OkwRP/jPzsCWnoGqg6DSGUiJrVQdQhERGrFfv58wMRE1WEQEX2UCA5WdQh5iiNBiIiIiIiIiEgtsAhCRERERERERGqBRRAiIiIiIiIiUgssghARERERERGRWmARhIiIiIiIiIjUAosgRERERERERKQWClwRZMqUKahcufIX9REdHQ2ZTIZLly7lSUzKhIeHw9TUNMcxREZGQiaT4eXLl/kWU37Li2vzoQ/Py6fOKxERUV5i3lFwMe8gIqL8kOsiSGxsLHr37o2SJUtCR0cHDg4OGDZsGJ49e5brnctkMmzfvl1h2ejRo3HgwIFc9/U+e3t7xMXFoUKFCl/UT16qU6cO4uLiYFKInwWfF9fmUzp37oybN2/m6z6IiKjwYN7xeZh35AzzDiIi9ZOrIsjdu3dRrVo13Lp1C7///jtu376NZcuW4cCBA6hduzaeP3/+xQEZGRnBwsLii/rQ1NSEtbU1tLS0vjievKKjowNra2vIZDKVxpGamvrZ2+bFtfkUfX19lChRIl/3QUREhQPzjs/HvCNnmHcQEamfXBVBBg0aBB0dHezduxfe3t4oVaoUmjVrhv379+Phw4f4/vvvpbaOjo6YNm0aAgICYGhoCFtbWyxZskRhPQD4+/tDJpNJ7z8c+hgUFIS2bdti5syZsLKygqmpKX744Qekp6djzJgxMDc3h52dHcLCwqRtPhwSGhQUBJlMluUVGRkJAEhJScHo0aNha2sLQ0ND1KxZU1onFx4ejlKlSsHAwAD+/v65/gYqu+GXERERcHd3h5GREfz8/BAXF6ew3a+//gp3d3fo6enBzc0NS5cuVVg/btw4lC1bFgYGBihdujQmTZqEtLQ0ab38fP76669wcnKCnp6e0vjk8Wzfvh1lypSBnp4efH19ERsbm6UvOfm1mTp1KiwtLWFsbIxvvvlGIeHJzMxESEgInJycoK+vDw8PD2zZsiXb8/ThsFT5PtesWQNHR0eYmJigS5cuePXq1WfvIyUlBYmJiQovIiIqeJh3MO8oCnkHwNyDiKggyXER5Pnz54iIiMC3334LfX19hXXW1tYIDAzExo0bIYSQlv/444/w8PDAxYsXMX78eAwbNgz79u0DAJw9exYAEBYWhri4OOm9MgcPHsSjR49w5MgRzJ8/H8HBwWjZsiXMzMxw+vRpfPPNNxgwYAAePHigdPtFixYhLi5Oeg0bNgwlSpSAm5sbAGDw4ME4efIkNmzYgCtXrqBjx47w8/PDrVu3AACnT59Gnz59MHjwYFy6dAkNGzbE9OnTc3rqsvX69WvMnTsXa9aswZEjRxATE4PRo0dL69etW4fJkydjxowZiIqKwsyZMzFp0iSsWrVKalOsWDGEh4fj+vXrWLRoEVasWIEFCxYo7Of27dv4448/sHXr1o/er/z69WvMmDEDq1evxvHjx/Hy5Ut06dLlo8dw4MABREVFITIyEr///ju2bt2KqVOnSutDQkKwevVqLFu2DP/88w9GjBiBbt264fDhwzk+T3fu3MH27duxY8cO7NixA4cPH8asWbM+ex8hISEwMTGRXvb29jmOhYiIvg7mHcw7lCmMeYd8G+YeREQFQ47Hbd66dQtCCLi7uytd7+7ujhcvXuDp06fSsEIvLy+MHz8eAFC2bFkcP34cCxYsQJMmTWBpaQkAMDU1hbW19Uf3bW5ujsWLF0NDQwOurq6YM2cOXr9+je+++w4AMGHCBMyaNQvHjh1T+uEp/8ABgK1bt2L58uXYv38/rK2tERMTg7CwMMTExKBkyZIA3t2DumfPHoSFhWHmzJlYtGgR/Pz8MHbsWOlYTpw4gT179uT09CmVlpaGZcuWwdnZGcC7pOiHH36Q1gcHB2PevHlo164dAMDJyQnXr1/H8uXL0bNnTwDAxIkTpfaOjo4YPXo0NmzYIMUKvBuKunr1aumcfyye0NBQ1KxZEwCwatUquLu748yZM6hRo4bSbXR0dLBy5UoYGBigfPny+OGHHzBmzBhMmzYNaWlpmDlzJvbv34/atWsDAEqXLo1jx45h+fLl8Pb2ztF5yszMRHh4OIoVKwYA6N69Ow4cOIAZM2YgJSUl1/uYMGECRo4cKb1PTExkMkJEVMAw72DeoUxhzDsA5h5ERAVJrm9eff8bl0+Rfzi8/37hwoW53SXKly8PDY3/H7RiZWWlMPmYpqYmLCwsEB8f/9F+Ll68iO7duyM0NBReXl4AgKtXryIjIwNly5ZVaJuSkiLdhxoVFQV/f/8sx/KlyYiBgYGUiACAjY2NdAzJycm4c+cO+vTpg379+klt0tPTFSY527hxIxYvXow7d+4gKSkJ6enpMDY2VtiPg4PDJxMRANDS0kL16tWl925ubjA1NUVUVFS2yYiHhwcMDAyk97Vr10ZSUhJiY2ORlJSE169fo0mTJgrbpKamwtPT85PxyDk6OkqJCKB4nm7fvp3rfejq6kJXVzfH+yciItVh3vH/x8K8o3DmHQBzDyKigiTHRRAXFxfIZDKlH8zAuw9sMzOzHH3o5Za2trbCe5lMpnRZZmZmtn08fvwYrVu3Rt++fdGnTx9peVJSEjQ1NXH+/HloamoqbGNkZJQH0WdP2THIk72kpCQAwIoVK6RvSOTkcZ48eRKBgYGYOnUqfH19YWJigg0bNmDevHkK7Q0NDfPrED5Kfgw7d+6Era2twrrcJAIfu9Z5tQ8iIipYmHfkPeYdOcO8g4ioaMtxEcTCwgJNmjTB0qVLMWLECIX7cx8/fox169ahR48eCrOQnzp1SqGPU6dOKQxr1dbWRkZGxpfEnyNv375FmzZt4Obmhvnz5yus8/T0REZGBuLj41GvXj2l27u7u+P06dMKyz48trxmZWWFkiVL4u7duwgMDFTa5sSJE3BwcFCYGO7+/fufvc/09HScO3dO+vblxo0bePnyZbZDkQHg8uXLePPmjfTzcOrUKRgZGcHe3h7m5ubQ1dVFTExMjoeg5la5cuXyfR9ERPT1Me9g3qEM8w4iIvpSubodJjQ0FHXq1IGvry+mT58OJycn/PPPPxgzZgxsbW0xY8YMhfbHjx/HnDlz0LZtW+zbtw+bN2/Gzp07pfWOjo44cOAAvLy8oKurCzMzs7w5qg8MGDAAsbGxOHDgAJ4+fSotNzc3R9myZREYGIgePXpg3rx58PT0xNOnT3HgwAFUqlQJLVq0wNChQ+Hl5YW5c+eiTZs2iIiI+OIhqTkxdepUDB06FCYmJvDz80NKSgrOnTuHFy9eYOTIkShTpgxiYmKwYcMGVK9eHTt37sS2bds+e3/a2toYMmQIFi9eDC0tLQwePBi1atXKdkgq8G74Z58+fTBx4kRER0cjODgYgwcPhoaGBooVK4bRo0djxIgRyMzMRN26dZGQkIDjx4/D2NhYur/4S3yNfRARkWow72De8SHmHURE9KVy9YjcMmXK4Ny5cyhdujQ6deoEZ2dn9O/fHw0bNsTJkydhbm6u0H7UqFE4d+4cPD09MX36dMyfPx++vr7S+nnz5mHfvn2wt7fP1b2auXX48GHExcWhXLlysLGxkV4nTpwA8G6m+B49emDUqFFwdXVF27ZtcfbsWZQqVQoAUKtWLaxYsQKLFi2Ch4cH9u7dqzAxWH7p27cvfv31V4SFhaFixYrw9vZGeHg4nJycAACtW7fGiBEjMHjwYFSuXBknTpzApEmTPnt/BgYGGDduHLp27QovLy8YGRlh48aNH92mUaNGKFOmDOrXr4/OnTujdevWmDJlirR+2rRpmDRpEkJCQuDu7g4/Pz/s3LlTOoa88DX2QUREXx/zDuYdH2LeQUREX0omcjPjWC44Ojpi+PDhGD58eH50T3ksPDwcw4cPx8uXL3O8TVBQEF6+fInt27fnW1xfQ2JiIkxMTODz3SZo6Rl8egOiIipiUgtVh0BFjPzf14SEhCyTZ+Y15h2FizrnHcD//25gxAjgvYlniYgKIhEcrOoQciSneUeuRoIQERERERERERVWLIIQERERERERkVrIt9thiAoL3g5D9A5vh6G89jVvhyEqTHg7DBEVJkXtdphcPR2GqCjbNs6XSToRERF9NbEjR8LOzk7VYRARqRXeDkNEREREREREaoFFECIiIiIiIiJSCyyCEBEREREREZFaYBGEiIiIiIiIiNQCiyBEREREREREpBb4dBii//GfHcFH5FKhw8faEhEVXvbz5/MRuURUZBSWR+lyJAgRERERERERqQUWQYiIiIiIiIhILbAIQkRERERERERqgUUQIiIiIiIiIlILLIIQERERERERkVpgEYSIiIiIiIiI1AKLIKQyT58+xcCBA1GqVCno6urC2toavr6+OHz4MIoXL45Zs2Yp3W7atGmwsrJCWloawsPDIZPJIJPJoKGhARsbG3Tu3BkxMTFf+WiIiIioIGPeQUREAIsgpELt27fHxYsXsWrVKty8eRN//fUXGjRogISEBHTr1g1hYWFZthFCIDw8HD169IC2tjYAwNjYGHFxcXj48CH++OMP3LhxAx07dvzah0NEREQFGPMOIiICAC1VB0Dq6eXLlzh69CgiIyPh7e0NAHBwcECNGjUAAE5OTli0aBGOHTuGunXrStsdPnwYd+/eRZ8+faRlMpkM1tbWAAAbGxv06dMHQ4cORWJiIoyNjb/iUREREVFBxLyDiIjkOBKEVMLIyAhGRkbYvn07UlJSsqyvWLEiqlevjpUrVyosDwsLQ506deDm5qa03/j4eGzbtg2amprQ1NRU2iYlJQWJiYkKLyIiIiq6VJl3AMw9iIgKEhZBSCW0tLQQHh6OVatWwdTUFF5eXvjuu+9w5coVqU2fPn2wefNmJCUlAQBevXqFLVu2oHfv3gp9JSQkwMjICIaGhrCyssKhQ4cwaNAgGBoaKt13SEgITExMpJe9vX3+HSgRERGpnCrzDoC5BxFRQcIiCKlM+/bt8ejRI/z111/w8/NDZGQkqlSpgvDwcABAQEAAMjIysGnTJgDAxo0boaGhgc6dOyv0U6xYMVy6dAnnzp3DvHnzUKVKFcyYMSPb/U6YMAEJCQnSKzY2Nt+OkYiIiAoGVeUdAHMPIqKChEUQUik9PT00adIEkyZNwokTJxAUFITg4GAA7yYe69ChgzRRWVhYGDp16gQjIyOFPjQ0NODi4gJ3d3eMHDkStWrVwsCBA7Pdp66uLoyNjRVeREREVPSpIu8AmHsQERUkLIJQgVKuXDkkJydL7/v06YNjx45hx44dOHHihMLEZNkZP348Nm7ciAsXLuRnqERERFTIMe8gIlI/LIKQSjx79gw+Pj5Yu3Ytrly5gnv37mHz5s2YM2cO2rRpI7WrX78+XFxc0KNHD7i5uaFOnTqf7Nve3h7+/v6YPHlyfh4CERERFRLMO4iISI5FEFIJIyMj1KxZEwsWLED9+vVRoUIFTJo0Cf369UNoaKjUTiaToXfv3njx4kWWick+ZsSIEdi5cyfOnDmTH+ETERFRIcK8g4iI5GRCCKHqIIhUKTExESYmJvD5bhO09AxUHQ5RrkRMaqHqEIiyJf/3NSEhgXMgEL1H/ruBESMAExNVh0NElCfE/+ZYUpWc5h0cCUJEREREREREaoFFECIiIiIiIiJSCyyCEBEREREREZFaYBGEiIiIiIiIiNSClqoDICooto3z5cR9RERE9NXEjhwJOzs7VYdBRKRWOBKEiIiIiIiIiNQCiyBEREREREREpBZYBCEiIiIiIiIitcAiCBERERERERGpBRZBiIiIiIiIiEgt8OkwRP/jPzsCWnoGqg6DKEciJrVQdQhERPSF7OfPB0xMVB0GEVGeEcHBqg7hkzgShIiIiIiIiIjUAosgRERERERERKQWWAQhIiIiIiIiIrXAIggRERERERERqQUWQYiIiIiIiIhILbAIUoTIZDJs3779q+9XCIH+/fvD3NwcMpkMly5d+uoxEBER0dfH3IOIiAobFkHyUFBQEGQyGWQyGbS1teHk5ISxY8fi7du3qg4tX+3Zswfh4eHYsWMH4uLiUKFCBZXEER0dzUSIiIjUCnMP5h5ERJQ7WqoOoKjx8/NDWFgY0tLScP78efTs2RMymQyzZ89WdWj55s6dO7CxsUGdOnU+uw8hBDIyMqClxR9JIiKi3GDu8XmYexARqSeOBMljurq6sLa2hr29Pdq2bYvGjRtj37590vpnz54hICAAtra2MDAwQMWKFfH7778r9NGgQQMMHToUY8eOhbm5OaytrTFlyhSFNrdu3UL9+vWhp6eHcuXKKexD7urVq/Dx8YG+vj4sLCzQv39/JCUlSeuDgoLQtm1bzJw5E1ZWVjA1NcUPP/yA9PR0jBkzBubm5rCzs0NYWFi2xxsUFIQhQ4YgJiYGMpkMjo6OAICUlBQMHToUJUqUgJ6eHurWrYuzZ89K20VGRkImk2H37t2oWrUqdHV1cezYMWRmZiIkJAROTk7Q19eHh4cHtmzZIm334sULBAYGwtLSEvr6+ihTpowUn5OTEwDA09MTMpkMDRo0+PjFIiIiKgKYezgCYO5BREQ5w9J3Prp27RpOnDgBBwcHadnbt29RtWpVjBs3DsbGxti5cye6d+8OZ2dn1KhRQ2q3atUqjBw5EqdPn8bJkycRFBQELy8vNGnSBJmZmWjXrh2srKxw+vRpJCQkYPjw4Qr7Tk5Ohq+vL2rXro2zZ88iPj4effv2xeDBgxEeHi61O3jwIOzs7HDkyBEcP34cffr0wYkTJ1C/fn2cPn0aGzduxIABA9CkSRPY2dllOcZFixbB2dkZv/zyC86ePQtNTU0AwNixY/HHH39g1apVcHBwwJw5c+Dr64vbt2/D3Nxc2n78+PGYO3cuSpcuDTMzM4SEhGDt2rVYtmwZypQpgyNHjqBbt26wtLSEt7c3Jk2ahOvXr2P37t0oXrw4bt++jTdv3gAAzpw5gxo1amD//v0oX748dHR0lF6XlJQUpKSkSO8TExNzflGJiIgKMOYezD2IiOjjZEIIoeogioqgoCCsXbsWenp6SE9PR0pKCjQ0NLBp0ya0b98+2+1atmwJNzc3zJ07F8C7b2MyMjJw9OhRqU2NGjXg4+ODWbNmYe/evWjRogXu37+PkiVLAnh3b2yzZs2wbds2tG3bFitWrMC4ceMQGxsLQ0NDAMCuXbvQqlUrPHr0CFZWVggKCkJkZCTu3r0LDY13g4Lc3NxQokQJHDlyBACQkZEBExMT/Prrr+jSpYvS+BcuXIiFCxciOjoawLskyMzMDOHh4ejatSsAIC0tDY6Ojhg+fDjGjBmDyMhINGzYENu3b0ebNm0AvEsQzM3NsX//ftSuXVvqv2/fvnj9+jXWr1+P1q1bo3jx4li5cmWWOKKjo+Hk5ISLFy+icuXK2Z7vKVOmYOrUqVmW+3y3CVp6BtluR1SQRExqoeoQiD4pMTERJiYmSEhIgLGxsarDKZKYe0QDKLy5B0aMAExMst2OiKiwEcHBKtt3TvMOjgTJYw0bNsTPP/+M5ORkLFiwAFpaWgpJSEZGBmbOnIlNmzbh4cOHSE1NRUpKCgwMFP/4rlSpksJ7GxsbxMfHAwCioqJgb28vJSEAFD645W08PDykJAQAvLy8kJmZiRs3bsDKygoAUL58eSkJAQArKyuFycU0NTVhYWEh7Tsn7ty5g7S0NHh5eUnLtLW1UaNGDURFRSm0rVatmvT/t2/fxuvXr9GkSROFNqmpqfD09AQADBw4EO3bt8eFCxfQtGlTtG3bNtf3A0+YMAEjR46U3icmJsLe3j5XfRARERUUzD2YexARUc6xCJLHDA0N4eLiAgBYuXIlPDw88Ntvv6FPnz4AgB9//BGLFi3CwoULUbFiRRgaGmL48OFITU1V6EdbW1vhvUwmQ2ZmZp7Hq2w/X2vfABQSJfk9wzt37oStra1CO11dXQBAs2bNcP/+fezatQv79u1Do0aNMGjQIOmbrJzQ1dWV+iMiIirsmHvkDnMPIiL1xolR85GGhga+++47TJw4Ubp39Pjx42jTpg26desGDw8PlC5dGjdv3sxVv+7u7oiNjUVcXJy07NSpU1naXL58GcnJydKy48ePQ0NDA66url9wVJ/m7OwMHR0dHD9+XFqWlpaGs2fPoly5ctluV65cOejq6iImJgYuLi4Kr/e/LbG0tETPnj2xdu1aLFy4EL/88gsASPfhZmRk5NORERERFWzMPZh7EBHRx7EIks86duwITU1NLFmyBABQpkwZ7Nu3DydOnEBUVBQGDBiAJ0+e5KrPxo0bo2zZsujZsycuX76Mo0eP4vvvv1doExgYCD09PfTs2RPXrl3DoUOHMGTIEHTv3l0ajppfDA0NMXDgQIwZMwZ79uzB9evX0a9fP7x+/Vr6VkqZYsWKYfTo0RgxYgRWrVqFO3fu4MKFC/jpp5+watUqAMDkyZPx559/4vbt2/jnn3+wY8cOuLu7AwBKlCgBfX197NmzB0+ePEFCQkK+HicREVFBxNyDuQcREWWPRZB8pqWlhcGDB2POnDlITk7GxIkTUaVKFfj6+qJBgwawtrZG27Ztc9WnhoYGtm3bhjdv3qBGjRro27cvZsyYodDGwMAAEREReP78OapXr44OHTqgUaNGCA0NzcOjy96sWbPQvn17dO/eHVWqVMHt27cREREBMzOzj243bdo0TJo0CSEhIXB3d4efnx927twpPYJOR0cHEyZMQKVKlVC/fn1oampiw4YNAN6d68WLF2P58uUoWbKkNOkZERGROmHuwdyDiIiyx6fDkNqTzyLMp8NQYcKnw1BhwKfDECkn/93g02GIqKgpDE+H4UgQIiIiIiIiIlILLIIQERERERERkVpgEYSIiIiIiIiI1AKLIERERERERESkFrRUHQBRQbFtnC8n7iMiIqKvJnbkSNjZ2ak6DCIitcKRIERERERERESkFlgEISIiIiIiIiK1wCIIEREREREREakFFkGIiIiIiIiISC1wYlSi//GfHQEtPQNVh0FFXMSkFqoOgYiICgj7+fMBExNVh0FE9NWI4GBVh8CRIERERERERESkHlgEISIiIiIiIiK1wCIIEREREREREakFFkGIiIiIiIiISC2wCEJEREREREREaoFFECIiIiIiIiJSCwWuCDJlyhRUrlz5i/qIjo6GTCbDpUuX8iQmZcLDw2Fqappv/X8OmUyG7du3f/X9CiHQv39/mJub5/t5JyIiymvMPT4fcw8iIipscl0EiY2NRe/evVGyZEno6OjAwcEBw4YNw7Nnz3K9c2UfnKNHj8aBAwdy3df77O3tERcXhwoVKnxRP5Qze/bsQXh4OHbs2KHS8/41ElAiIvr6mHvQh5h7EBHR58pVEeTu3buoVq0abt26hd9//x23b9/GsmXLcODAAdSuXRvPnz//4oCMjIxgYWHxRX1oamrC2toaWlpaXxxPXkpLS1N1CPnizp07sLGxQZ06dT77vAshkJ6eng/RERFRYcbc48sw98gecw8iIvWUqyLIoEGDoKOjg71798Lb2xulSpVCs2bNsH//fjx8+BDff/+91NbR0RHTpk1DQEAADA0NYWtriyVLliisBwB/f3/IZDLp/YdDUoOCgtC2bVvMnDkTVlZWMDU1xQ8//ID09HSMGTMG5ubmsLOzQ1hYmLTNh1X5oKAgyGSyLK/IyEgAQEpKCkaPHg1bW1sYGhqiZs2a0jq58PBwlCpVCgYGBvD39//kt0/yGDZu3Ahvb2/o6elh3bp1ePbsGQICAmBrawsDAwNUrFgRv//+u8K2DRo0wNChQzF27FiYm5vD2toaU6ZMUWhz69Yt1K9fH3p6eihXrhz27duXJYarV6/Cx8cH+vr6sLCwQP/+/ZGUlPRF5/ZDQUFBGDJkCGJiYhSuY0pKCoYOHYoSJUpAT08PdevWxdmzZ6XtIiMjIZPJsHv3blStWhW6uro4duwYMjMzERISAicnJ+jr68PDwwNbtmyRtnvx4gUCAwNhaWkJfX19lClTRorPyckJAODp6QmZTIYGDRp89BoREVHBx9yDuceHmHsQEdGXyHER5Pnz54iIiMC3334LfX19hXXW1tYIDAzExo0bIYSQlv/444/w8PDAxYsXMX78eAwbNkz6wJR/KIWFhSEuLk7hQ+pDBw8exKNHj3DkyBHMnz8fwcHBaNmyJczMzHD69Gl88803GDBgAB48eKB0+0WLFiEuLk56DRs2DCVKlICbmxsAYPDgwTh58iQ2bNiAK1euoGPHjvDz88OtW7cAAKdPn0afPn0wePBgXLp0CQ0bNsT06dNzdN7kxx0VFQVfX1+8ffsWVatWxc6dO3Ht2jX0798f3bt3x5kzZxS2W7VqFQwNDXH69GnMmTMHP/zwg3TuMjMz0a5dO+jo6OD06dNYtmwZxo0bp7B9cnIyfH19YWZmhrNnz2Lz5s3Yv38/Bg8enOfn9ocffoCdnZ3CdRw7diz++OMPrFq1ChcuXICLiwt8fX2zfGM3fvx4zJo1C1FRUahUqRJCQkKwevVqLFu2DP/88w9GjBiBbt264fDhwwCASZMm4fr169i9ezeioqLw888/o3jx4gAgncP9+/cjLi4OW7duVRpzSkoKEhMTFV5ERFTwMPdg7pHduWXuQUREn0sm3s8cPuL06dOoVasWtm3bhrZt22ZZv2DBAowcORJPnjxBiRIl4OjoCHd3d+zevVtq06VLFyQmJmLXrl3vdi6TZelvypQp2L59u8I3KZGRkbh79y40NN7VbNzc3FCiRAkcOXIEAJCRkQETExP8+uuv6NKlC6Kjo+Hk5ISLFy9mmehs69atCAwMxP79++Hl5YWYmBiULl0aMTExKFmypNSucePGqFGjBmbOnImuXbsiISEBO3fuVDiWPXv24OXLl0rPlzyGhQsXYtiwYR89ty1btoSbmxvmzp0L4N23MRkZGTh69KjUpkaNGvDx8cGsWbOwd+9etGjRAvfv35di3rNnD5o1ayadzxUrVmDcuHGIjY2FoaEhAGDXrl1o1aoVHj16BCsrq886t8osXLgQCxcuRHR0NIB3SZCZmRnCw8PRtWtXAO+G4zo6OmL48OEYM2YMIiMj0bBhQ2zfvh1t2rQB8C5BMDc3x/79+1G7dm2p/759++L169dYv349WrdujeLFi2PlypXZnnNl1/19U6ZMwdSpU7Ms9/luE7T0DLLdjigvRExqoeoQiL6axMREmJiYICEhAcbGxrnenrkHc4+inntgxAjAxCTb7YiIihoRHJxvfec078j1xKg5rJkAgMKHifx9VFRUbneJ8uXLSx+UAGBlZYWKFStK7zU1NWFhYYH4+PiP9nPx4kV0794doaGh8PLyAvBu2GZGRgbKli0LIyMj6XX48GHcuXMHABAVFYWaNWt+9NiyU61aNYX3GRkZmDZtGipWrAhzc3MYGRkhIiICMTExCu0qVaqk8N7GxkY6vqioKNjb2yskTh/GExUVBQ8PDykJAQAvLy9kZmbixo0b0rK8Orfvu3PnDtLS0qRzDADa2tqoUaNGluv//vm5ffs2Xr9+jSZNmihci9WrV0vXYuDAgdiwYQMqV66MsWPH4sSJEzmOS27ChAlISEiQXrGxsbnug4iIvh7mHsqPLTvMPd5h7kFERMrkeBYpFxcXyGQyREVFwd/fP8v6qKgomJmZwdLSMk8DBN59iL1PJpMpXZaZmZltH48fP0br1q3Rt29f9OnTR1qelJQETU1NnD9/HpqamgrbGBkZfXHs7ycCwLthuosWLcLChQtRsWJFGBoaYvjw4UhNTVVol9vj+1x5cW6/xPvnR37P8M6dO2Fra6vQTldXFwDQrFkz3L9/H7t27cK+ffvQqFEjDBo0SPomKyd0dXWl/oiIqOBi7vF5mHt8HHMPIiL1luORIBYWFmjSpAmWLl2KN2/eKKx7/Pgx1q1bh86dO0Mmk0nLT506pdDu1KlTcHd3l95ra2sjIyPjc2PPsbdv36JNmzZwc3PD/PnzFdZ5enoiIyMD8fHxcHFxUXhZW1sDANzd3XH69Oksx/I5jh8/jjZt2qBbt27w8PBA6dKlcfPmzVz14e7ujtjYWMTFxWUbj7u7Oy5fvozk5GSFfWtoaMDV1fWzYs8pZ2dn6Ojo4Pjx49KytLQ0nD17FuXKlct2u3LlykFXVxcxMTFZroW9vb3UztLSEj179sTatWuxcOFC/PLLLwAAHR0dAPgqP1NERJT/mHsw98gp5h5ERJRTubodJjQ0FCkpKfD19cWRI0cQGxuLPXv2oEmTJrC1tcWMGTMU2h8/fhxz5szBzZs3sWTJEmzevFnhHlVHR0ccOHAAjx8/xosXL/LmiJQYMGAAYmNjsXjxYjx9+hSPHz/G48ePkZqairJlyyIwMBA9evTA1q1bce/ePZw5cwYhISHSfbhDhw7Fnj17MHfuXNy6dQuhoaHYs2fPZ8VSpkwZ7Nu3DydOnEBUVBQGDBiAJ0+e5KqPxo0bo2zZsujZsycuX76Mo0ePKsyODwCBgYHQ09NDz549ce3aNRw6dAhDhgxB9+7dYWVl9Vmx55ShoSEGDhyIMWPGYM+ePbh+/Tr69euH169fK3wT9qFixYph9OjRGDFiBFatWoU7d+7gwoUL+Omnn7Bq1SoAwOTJk/Hnn3/i9u3b+Oeff7Bjxw4puS1RogT09fWxZ88ePHnyBAkJCfl6nERElP+YezD3yAnmHkRElFO5KoKUKVMG586dQ+nSpdGpUyc4Ozujf//+aNiwIU6ePAlzc3OF9qNGjcK5c+fg6emJ6dOnY/78+fD19ZXWz5s3D/v27YO9vT08PT3z5oiUOHz4MOLi4lCuXDnY2NhIL/k9nWFhYejRowdGjRoFV1dXtG3bFmfPnkWpUqUAALVq1cKKFSuwaNEieHh4YO/evZg4ceJnxTJx4kRUqVIFvr6+aNCgAaytrZVO9vYxGhoa2LZtG968eYMaNWqgb9++WZJAAwMDRERE4Pnz56hevTo6dOiARo0aITQ09LPizq1Zs2ahffv26N69O6pUqYLbt28jIiICZmZmH91u2rRpmDRpEkJCQuDu7g4/Pz/s3LlTegSdjo4OJkyYgEqVKqF+/frQ1NTEhg0bAABaWlpYvHgxli9fjpIlS0qTnhERUeHF3IO5R04x9yAiopzI8dNhcks+G/fw4cPzo3uiPCOfRZhPh6GvgU+HIXXypU+HyS3mHlRYyH83+HQYIlI3hfLpMEREREREREREhRGLIERERERERESkFnL8iNzcio6Ozq+uiYiIiLJg7kFERESfwpEgRERERERERKQW8m0kCFFhs22c71eZuI+IiIgIAGJHjoSdnZ2qwyAiUiscCUJEREREREREaoFFECIiIiIiIiJSCyyCEBEREREREZFaYBGEiIiIiIiIiNQCJ0Yl+h//2RHQ0jNQdRhUgERMaqHqEIiIqAiznz8fMDFRdRhERAWGCA7O931wJAgRERERERERqQUWQYiIiIiIiIhILbAIQkRERERERERqgUUQIiIiIiIiIlILLIIQERERERERkVpgEYSIiIiIiIiI1AKLIERERERERESkFlgEIZV4/Pgxhg0bBhcXF+jp6cHKygpeXl74+eef8fr1awCAo6MjZDIZZDIZDA0NUaVKFWzevFnqY8qUKdJ6TU1N2Nvbo3///nj+/LmqDouIiIgKKOYeREQEsAhCKnD37l14enpi7969mDlzJi5evIiTJ09i7Nix2LFjB/bv3y+1/eGHHxAXF4eLFy+ievXq6Ny5M06cOCGtL1++POLi4hATE4OwsDDs2bMHAwcOVMVhERERUQHF3IOIiOS0VB0AqZ9vv/0WWlpaOHfuHAwNDaXlpUuXRps2bSCEkJYVK1YM1tbWsLa2xpIlS7B27Vr8/fffqFOnDgBAS0sL1tbWAABbW1t07NgRYWFhH91/SkoKUlJSpPeJiYl5eXhERERUwDD3ICIiOY4Eoa/q2bNn2Lt3LwYNGqSQhLxPJpMpXa6lpQVtbW2kpqYqXR8dHY2IiAjo6Oh8NIaQkBCYmJhIL3t7+9wdBBERERUazD2IiOh9LILQV3X79m0IIeDq6qqwvHjx4jAyMoKRkRHGjRuXZbvU1FSEhIQgISEBPj4+0vKrV6/CyMgI+vr6cHJywj///KN0+/dNmDABCQkJ0is2NjZvDo6IiIgKHOYeRET0Pt4OQwXCmTNnkJmZicDAQIXhouPGjcPEiRPx9u1bGBkZYdasWWjRooW03tXVFX/99Rfevn2LtWvX4tKlSxgyZMhH96WrqwtdXd18OxYiIiIq+Jh7EBGpJ44Eoa/KxcUFMpkMN27cUFheunRpuLi4QF9fX2H5mDFjcOnSJTx48AAvXrzI8k2Ljo4OXFxcUKFCBcyaNQuampqYOnVqvh8HERERFQ7MPYiI6H0sgtBXZWFhgSZNmiA0NBTJycmfbF+8eHG4uLjA2to62/t13zdx4kTMnTsXjx49yotwiYiIqJBj7kFERO9jEYS+uqVLlyI9PR3VqlXDxo0bERUVhRs3bmDt2rX4999/oamp+dl9165dG5UqVcLMmTPzMGIiIiIqzJh7EBGRHIsg9NU5Ozvj4sWLaNy4MSZMmAAPDw9Uq1YNP/30E0aPHo1p06Z9Uf8jRozAr7/+yknHiIiICABzDyIi+n8y8f6D0YnUUGJiIkxMTODz3SZo6RmoOhwqQCImtfh0IyLKlvzf14SEBBgbG6s6HKICQ/67gREjABMTVYdDRFRgiODgz942p3kHR4IQERERERERkVpgEYSIiIiIiIiI1AKLIERERERERESkFrRUHQBRQbFtnC/vWSciIqKvJnbkSNjZ2ak6DCIitcKRIERERERERESkFlgEISIiIiIiIiK1wCIIEREREREREakFFkGIiIiIiIiISC2wCEJEREREREREaoFFECIiIiIiIiJSCyyCEBEREREREZFaYBGEiIiIiIiIiNQCiyBEREREREREpBZYBCEiIiIiIiIitcAiCBERERERERGpBS1VB0CkakIIAEBiYqKKIyEiKlrk/67K/50lonfkvxOvXr1i/kFElEdymnewCEJq79mzZwAAe3t7FUdCRFQ0vXr1CiYmJqoOg6jAkOce5cqVU3EkRERFz6fyDhZBSO2Zm5sDAGJiYtQiSU9MTIS9vT1iY2NhbGys6nDylTodK8DjLcoK67EKIfDq1SuULFlS1aEQFSjqlnvkVGH9ty6/8bwox/OinDqfl5zmHSyCkNrT0Hg3NY6JiYla/UNhbGysNserTscK8HiLssJ4rPwDjygrdc09cqow/lv3NfC8KMfzopy6npec5B2cGJWIiIiIiIiI1AKLIERERERERESkFlgEIbWnq6uL4OBg6OrqqjqUr0KdjledjhXg8RZl6nSsROqAv9PK8bwox/OiHM+LcjwvnyYTfG4dEREREREREakBjgQhIiIiIiIiIrXAIggRERERERERqQUWQYiIiIiIiIhILbAIQkRERERERERqgUUQUntLliyBo6Mj9PT0ULNmTZw5c0bVIeW5kJAQVK9eHcWKFUOJEiXQtm1b3LhxQ9VhfTWzZs2CTCbD8OHDVR1Kvnn48CG6desGCwsL6Ovro2LFijh37pyqw8pzGRkZmDRpEpycnKCvrw9nZ2dMmzYNRWWO7yNHjqBVq1YoWbIkZDIZtm/frrBeCIHJkyfDxsYG+vr6aNy4MW7duqWaYInoo3KbX2zevBlubm7Q09NDxYoVsWvXrq8U6deVm/OyYsUK1KtXD2ZmZjAzM0Pjxo2LZJ4GfH4+umHDBshkMrRt2zZ/A1SR3J6Xly9fYtCgQbCxsYGuri7Kli1bJH+XcnteFi5cCFdXV+jr68Pe3h4jRozA27dvv1K0BQ+LIKTWNm7ciJEjRyI4OBgXLlyAh4cHfH19ER8fr+rQ8tThw4cxaNAgnDp1Cvv27UNaWhqaNm2K5ORkVYeW786ePYvly5ejUqVKqg4l37x48QJeXl7Q1tbG7t27cf36dcybNw9mZmaqDi3PzZ49Gz///DNCQ0MRFRWF2bNnY86cOfjpp59UHVqeSE5OhoeHB5YsWaJ0/Zw5c7B48WIsW7YMp0+fhqGhIXx9fdU6kSEqiHKbX5w4cQIBAQHo06cPLl68iLZt26Jt27a4du3aV448f+X2vERGRiIgIACHDh3CyZMnYW9vj6ZNm+Lhw4dfOfL89bn5aHR0NEaPHo169ep9pUi/rtyel9TUVDRp0gTR0dHYsmULbty4gRUrVsDW1vYrR56/cnte1q9fj/HjxyM4OBhRUVH47bffsHHjRnz33XdfOfICRBCpsRo1aohBgwZJ7zMyMkTJkiVFSEiICqPKf/Hx8QKAOHz4sKpDyVevXr0SZcqUEfv27RPe3t5i2LBhqg4pX4wbN07UrVtX1WF8FS1atBC9e/dWWNauXTsRGBiooojyDwCxbds26X1mZqawtrYWP/74o7Ts5cuXQldXV/z+++8qiJCIspPb/KJTp07i/9q7z7AqjjUO4P9DbwcQkaoC4kVQURGUC3gFlAgWgt7YETFgxytoNCExBpWgmNhNgh00FtTYiB2VgzQVEbAhSDd5KIJYEKXO/eBlL0eKcAIcA+/vefbDmZ3dfWc5e2aYndkdO3asUJqlpSWbN29em8bZ3v5qu6uqqorx+Xy2f//+tgpRLEQ5L1VVVcza2prt2bOHubu7MxcXl3aItH219LwEBQWxXr16sYqKivYKUSxael68vLzYiBEjhNKWLl3KbGxs2jTOjxmNBCGdVkVFBRISEuDg4MClSUhIwMHBAXFxcWKMrO29ePECAKCmpibmSNqWl5cXxo4dK/Q37ojCwsJgYWGBSZMmQUNDA2ZmZti9e7e4w2oT1tbWuHr1KtLS0gAAycnJiI6OxujRo8UcWdvLyspCfn6+0PdZRUUFlpaWHf43i5C/E1HaF3FxcfXqKkdHxw51bbdGu6usrAyVlZUdqv0i6nlZs2YNNDQ04Onp2R5htjtRzktYWBisrKzg5eUFTU1N9O/fH2vXrkV1dXV7hd3mRDkv1tbWSEhI4KbMZGZm4vz58xgzZky7xPwxkhJ3AISIS1FREaqrq6GpqSmUrqmpiUePHokpqrZXU1MDHx8f2NjYoH///uIOp82Ehobizp07iI+PF3cobS4zMxNBQUFYunQpvvnmG8THx2Px4sWQkZGBu7u7uMNrVb6+vnj58iWMjY0hKSmJ6upqBAQEwNXVVdyhtbn8/HwAaPA3q3YdIUT8RGlf5Ofnd/hruzXaXV999RV0dHQ61M0NUc5LdHQ09u7di6SkpHaIUDxEOS+ZmZm4du0aXF1dcf78eaSnp2PhwoWorKyEn59fe4Td5kQ5L9OnT0dRURGGDRsGxhiqqqowf/78Tj0dhjpBCOlkvLy8cP/+fURHR4s7lDbz5MkTeHt7Izw8HHJycuIOp83V1NTAwsICa9euBQCYmZnh/v372LFjR4frBDl27BgOHTqEw4cPo1+/fkhKSoKPjw90dHQ6XFkJIYT8X2BgIEJDQyEQCDpF3d6YV69ewc3NDbt374a6urq4w/mo1NTUQENDA7t27YKkpCTMzc3x559/4scff+wwnSCiEAgEWLt2LX755RdYWloiPT0d3t7e8Pf3x8qVK8UdnlhQJwjptNTV1SEpKYmCggKh9IKCAmhpaYkpqra1aNEinD17FtevX0f37t3FHU6bSUhIQGFhIQYPHsylVVdX4/r16/jpp59QXl4OSUlJMUbYurS1tdG3b1+hNBMTE5w4cUJMEbWd5cuXw9fXF1OnTgUAmJqaIicnB+vWrevwnSC1v0sFBQXQ1tbm0gsKCjBo0CAxRUUIeZ8o7QstLa0O3x75K+2uDRs2IDAwEFeuXOlwDzpv6XnJyMhAdnY2nJ2dubSamhoAgJSUFFJTU2FoaNi2QbcDUb4v2trakJaWFmrjmZiYID8/HxUVFZCRkWnTmNuDKOdl5cqVcHNzw+zZswG8azu9fv0ac+fOxYoVKyAh0fmekNH5SkzI/8jIyMDc3BxXr17l0mpqanD16lVYWVmJMbLWxxjDokWLcOrUKVy7dg0GBgbiDqlNjRw5Evfu3UNSUhK3WFhYwNXVFUlJSR2qAwQAbGxs6r3yOC0tDXp6emKKqO2UlZXVq6wlJSW5BmBHZmBgAC0tLaHfrJcvX+LmzZsd7jeLkL8zUdoXVlZWQvkBIDw8vENd26K2u3744Qf4+/vj4sWLsLCwaI9Q21VLz4uxsXG9Ns6nn34Ke3t7JCUloUePHu0ZfpsR5ftiY2OD9PR0oTZBWloatLW1O0QHCCDaeWms7QS8+x+hUxLzg1kJEavQ0FAmKyvLQkJC2MOHD9ncuXOZqqoqy8/PF3dorWrBggVMRUWFCQQClpeXxy1lZWXiDq3ddOS3w9y6dYtJSUmxgIAA9vjxY3bo0CGmoKDADh48KO7QWp27uzvT1dVlZ8+eZVlZWezkyZNMXV2dffnll+IOrVW8evWKJSYmssTERAaAbdq0iSUmJrKcnBzGGGOBgYFMVVWVnTlzht29e5e5uLgwAwMD9ubNGzFHTgip60PtCzc3N+br68vlj4mJYVJSUmzDhg0sJSWF+fn5MWlpaXbv3j1xFaFNtPS8BAYGMhkZGfbbb78JtV9evXolriK0iZael/d11LfDtPS85ObmMj6fzxYtWsRSU1PZ2bNnmYaGBvv+++/FVYQ20dLz4ufnx/h8Pjty5AjLzMxkly9fZoaGhmzy5MniKoLYUScI6fS2b9/OevbsyWRkZNjQoUPZjRs3xB1SqwPQ4BIcHCzu0NpNR+4EYYyx33//nfXv35/JysoyY2NjtmvXLnGH1CZevnzJvL29Wc+ePZmcnBzr1asXW7FiBSsvLxd3aK0iIiKiwWvV3d2dMfbuNbkrV65kmpqaTFZWlo0cOZKlpqaKN2hCSIOaal/Y2tpy13WtY8eOMSMjIyYjI8P69evHzp07184Rt4+WnBc9Pb0GfxP9/PzaP/A21tLvS10dtROEsZafl9jYWGZpaclkZWVZr169WEBAAKuqqmrnqNteS85LZWUlW7VqFTM0NGRycnKsR48ebOHChaykpKT9A/9I8BjrrGNgCCGEEEIIIYQQ0pnQM0EIIYQQQgghhBDSKVAnCCGEEEIIIYQQQjoF6gQhhBBCCCGEEEJIp0CdIIQQQgghhBBCCOkUqBOEEEIIIYQQQgghnQJ1ghBCCCGEEEIIIaRToE4QQgghhBBCCCGEdArUCUIIIYQQQgghhJBOgTpBCCGkCRUVFejduzdiY2PFFsPUqVOxcePGVtmXnZ0dfHx8WmVfbSkkJASqqqoib8/j8XD69OlWi4cQQgj52DWn7pw1axbGjx/fov3q6+tjy5YtIsfVEqtWrcKgQYNE2jY7Oxs8Hg9JSUmtGhPpeKgThJBOhMfjNbmsWrXqL+27Of901j2esrIyhgwZgjNnzgjlCQkJAY/Hg4mJSb3tjx8/Dh6PB319fS6turoagYGBMDY2hry8PNTU1GBpaYk9e/ZweWbNmtVgmZ2cnJqMd8eOHTAwMIC1tXW9Mty4cUMob3l5Obp27QoejweBQNBgmVVUVGBjY4Nr16598FzV+vbbbxEQEIAXL140e5u/uylTpiAtLU2sMTTUUKQGFiGE/D38Hdo8paWlcHJygr29PUxMTBASEiJyTM21devWdjmOqJYtW4arV6+KNYaGbhgJBALweDw8f/5cLDGR1kWdIIR0Inl5edyyZcsWKCsrC6UtW7asXeIIDg5GXl4ebt++DRsbG0ycOBH37t0TyqOoqIjCwkLExcUJpe/duxc9e/YUSlu9ejU2b94Mf39/PHz4EBEREZg7d269isrJyUmovHl5eThy5EijcTLG8NNPP8HT07Peuh49eiA4OFgo7dSpU1BSUmqyzDExMVBXV8e4ceOQmZnZ6LHr6t+/PwwNDXHw4MFm5f87qaioaDBdXl4eGhoa7RxN+6qsrBR3CIQQ0mH9Hdo8CgoKOHv2LCIiIrB7924EBQW1eTwqKip/aaRla2ms/ldSUkLXrl3bOZr2wxhDVVWVuMPo9KgThJBOREtLi1tUVFTA4/GE0kJDQ2FiYgI5OTkYGxvjl19+4batqKjAokWLoK2tDTk5Oejp6WHdunUAwI3KmDBhQr1RGg1RVVWFlpYWjIyM4O/vj6qqKkRERAjlkZKSwvTp07Fv3z4u7Y8//oBAIMD06dOF8oaFhWHhwoWYNGkSDAwMMHDgQHh6etZr4MjKygqVV0tLC126dGk0zoSEBGRkZGDs2LH11rm7uyM0NBRv3rzh0vbt2wd3d/cmy9y/f38EBQXhzZs3CA8PR3FxMaZNmwZdXV0oKCjA1NS0wY4ZZ2dnhIaGNhqrqEpKSjBz5kx06dIFCgoKGD16NB4/fgzgXUXdrVs3/Pbbb1z+QYMGQVtbm/scHR0NWVlZlJWVAQCeP3+O2bNno1u3blBWVsaIESOQnJzM5a8d5rpnzx4YGBhATk6uwbjeH9KbnJwMe3t78Pl8KCsrw9zcHLdv326ybHl5eRg9ejTk5eXRq1cvoXIAwJMnTzB58mSoqqpCTU0NLi4uyM7O5uLcv38/zpw5w93FEwgEMDAwAACYmZmBx+PBzs6O29+ePXsavX5qR5AcPXoUtra2kJOTw6FDh5qMnxBCiOj+Dm0eCQkJSElJobCwEN999x22bt3aKmW/dOkSTExMoKSkxN0AqvX+KMdXr17B1dUVioqK0NbWxubNmxscCVFWVgYPDw/w+Xz07NkTu3btElrfVJ1a97gBAQHQ0dFBnz59Goz9/ekwAoEAQ4cOhaKiIlRVVWFjY4OcnJwmy//o0SNYW1tDTk4O/fv3R2RkpND6+/fvY/To0VBSUoKmpibc3NxQVFTExRkZGYmtW7dy9X92djbs7e0BAF26dAGPx8OsWbMAADU1NVi3bh0MDAwgLy+PgQMHCrU3akeQXLhwAebm5pCVlUV0dHST8ZO2R50ghBAAwKFDh/Ddd98hICAAKSkpWLt2LVauXIn9+/cDALZt24awsDAcO3YMqampOHToEFfxx8fHA/j/3Y7azx9SVVWFvXv3AgBkZGTqrffw8MCxY8e4f7BDQkLg5OQETU1NoXxaWlq4du0anj59KlLZGxMVFQUjIyPw+fx668zNzaGvr48TJ04AAHJzc3H9+nW4ubl9cL/y8vIA3jWy3r59C3Nzc5w7dw7379/H3Llz4ebmhlu3bgltM3ToUNy6dQvl5eWtULL/mzVrFm7fvo2wsDDExcWBMYYxY8agsrISPB4Pw4cP56b2lJSUICUlBW/evMGjR48AAJGRkRgyZAgUFBQAAJMmTUJhYSEuXLiAhIQEDB48GCNHjsSzZ8+4Y6anp+PEiRM4efJks6eVuLq6onv37oiPj0dCQgJ8fX0hLS3d5DYrV67EZ599huTkZLi6umLq1KlISUkB8G4UhqOjI/h8PqKiohATE8M1FisqKrBs2TJMnjxZaPSQtbU193e5cuUK8vLycPLkSQAfvn5q+fr6wtvbGykpKXB0dGxW2QkhhLSuj6nNc+PGDUybNg3btm3DP//5z79ctrKyMmzYsAG//vorrl+/jtzc3CZHvSxduhQxMTEICwtDeHg4oqKicOfOnXr5Nm7cCAsLCyQmJmLhwoVYsGABUlNTAXy4Tq119epVpKamIjw8HGfPnv1gWaqqqjB+/HjY2tri7t27iIuLw9y5c8Hj8Zrcbvny5fjiiy+QmJgIKysrODs7o7i4GMC7mzUjRoyAmZkZbt++jYsXL6KgoACTJ08G8G66kJWVFebMmcPV/z169ODae6mpqcjLy+M6rNatW4cDBw5gx44dePDgAZYsWYIZM2bU63jx9fVFYGAgUlJSMGDAgA+WnbQxRgjplIKDg5mKigr32dDQkB0+fFgoj7+/P7OysmKMMfaf//yHjRgxgtXU1DS4PwDs1KlTHzwuACYnJ8cUFRWZhIQEA8D09fVZcXFxg7ENGjSI7d+/n9XU1DBDQ0N25swZtnnzZqanp8flf/DgATMxMWESEhLM1NSUzZs3j50/f17ouO7u7kxSUpIpKioKLQEBAY3G6u3tzUaMGNFoWbds2cLs7e0ZY4ytXr2aTZgwgZWUlDAALCIiosFz8/r1a7Zw4UImKSnJkpOTGzzu2LFj2RdffCGUlpyczACw7OzsRuNtDltbW+bt7c0YYywtLY0BYDExMdz6oqIiJi8vz44dO8YYY2zbtm2sX79+jDHGTp8+zSwtLZmLiwsLCgpijDHm4ODAvvnmG8YYY1FRUUxZWZm9fftW6JiGhoZs586djDHG/Pz8mLS0NCssLGwyzve/n3w+n4WEhDS7nADY/PnzhdIsLS3ZggULGGOM/frrr6xPnz5C3+fy8nImLy/PLl26xBh7951xcXER2kdWVhYDwBITE+uVsanrp3a7LVu2NLsMhBBCWsfH2uYpKChgUlJSrF+/fszS0pI5OjqKVsD/CQ4OZgBYeno6l/bzzz8zTU1N7nPduu3ly5dMWlqaHT9+nFv//PlzpqCgwLUVGGNMT0+PzZgxg/tcU1PDNDQ0uLZAc+tUTU1NVl5e3mQZ/Pz82MCBAxljjBUXFzMATCAQNKv8tXVtYGAgl1ZZWcm6d+/O1q9fzxh793ceNWqU0HZPnjxhAFhqaipjTLitVCsiIoIBYCUlJVza27dvmYKCAouNjRXK6+npyaZNmya03enTp5tVBtI+aCQIIQSvX79GRkYGPD09oaSkxC3ff/89MjIyALwbMZCUlIQ+ffpg8eLFuHz5ssjH27x5M5KSknDhwgX07dsXe/bsgZqaWoN5PTw8EBwcjMjISLx+/Rpjxoypl6dv3764f/8+bty4AQ8PDxQWFsLZ2RmzZ88Wymdvb4+kpCShZf78+Y3G+ebNm0anawDAjBkzEBcXh8zMTISEhMDDw6PRvNOmTYOSkhL4fD5OnDiBvXv3YsCAAaiuroa/vz9MTU2hpqYGJSUlXLp0Cbm5uULb144eqR0V87758+cL/e2aIyUlBVJSUrC0tOTSunbtij59+nAjJmxtbfHw4UM8ffoUkZGRsLOzg52dHQQCASorKxEbG8tNCUlOTkZpaSm6du0qFEtWVhb3PQIAPT09dOvWrVkx1lq6dClmz54NBwcHBAYGCu2vMVZWVvU+15YrOTkZ6enp4PP5XJxqamp4+/Zts/ZdV3Oun1oWFhYt2jchhJDW9TG1eTQ0NFBZWcm1YS5evNjgPqKiooRibWo6pYKCAgwNDbnP2traKCwsbDBvZmYmKisrMXToUC5NRUWlwakqdUcv1E4tqt1vc+tUU1PTBkf+NkZNTQ2zZs2Co6MjnJ2dsXXrVqGpPY2pW/9LSUnBwsJCqP6PiIgQOp/GxsYA0OL6Pz09HWVlZfjkk0+E9nfgwAGq/z9yUuIOgBAifqWlpQCA3bt3C/1DDACSkpIAgMGDByMrKwsXLlzAlStXMHnyZDg4ONR7zkJzaGlpoXfv3ujduzeCg4MxZswYPHz4sMEHYbq6uuLLL7/EqlWr4ObmBimphn+2JCQkMGTIEAwZMgQ+Pj44ePAg3NzcsGLFCu45DoqKiujdu3ez41RXV6/3wNa6unbtinHjxsHT0xNv377F6NGj8erVqwbzbt68GQ4ODlBRURHqAPjxxx+xdetWbNmyBaamplBUVISPj0+9B4bVTidprPNgzZo1bfKQt9rOmcjISERGRiIgIABaWlpYv3494uPjUVlZyb05p7S0FNra2kJvxqlV9/keioqKLY5j1apVmD59Os6dO4cLFy7Az88PoaGhmDBhgkjlKi0thbm5eYMNyZZ20DTn+qklStkJIYS0no+5zdMYCwsLoemj708Lruv9qaI8Hg+MsRbH3Zz91tTUAGh+nSpKHRgcHIzFixfj4sWLOHr0KL799luEh4eLPHWotLQUzs7OWL9+fb11dZ951tx9AcC5c+egq6srtE5WVlboM9X/HxfqBCGEQFNTEzo6OsjMzISrq2uj+ZSVlTFlyhRMmTIFEydOhJOTE549ewY1NTVIS0ujurq6xcceOnQozM3NERAQ0OADwdTU1PDpp5/i2LFj2LFjR7P327dvXwDv7viIyszMDEFBQWCMNTr/1MPDA2PGjMFXX31V7x/eumobQe+LiYmBi4sLZsyYAeDdA7bS0tK4+Gvdv38f3bt3h7q6eoP719DQaPHbVExMTFBVVYWbN29yHRnFxcVITU3ljs/j8fCvf/0LZ86cwYMHDzBs2DAoKCigvLwcO3fuhIWFBVexDx48GPn5+ZCSkvrgg+JEYWRkBCMjIyxZsgTTpk1DcHBwk50gN27cwMyZM4U+m5mZcbEePXoUGhoaUFZWbnB7GRmZet/p2jtYddObe/0QQggRv4+5zdMYeXn5Ft3Eaa5evXpBWloa8fHx3Jv3Xrx4gbS0NAwfPrzZ+2lOnfpXmJmZwczMDF9//TWsrKxw+PDhJjtBbty4wcVfVVWFhIQELFq0iIv1xIkT0NfXb/TGWnPr/759+0JWVha5ubmwtbX9S2Uk7YumwxBCALx7zey6deuwbds2pKWl4d69ewgODsamTZsAAJs2bcKRI0fw6NEjpKWl4fjx49DS0uLu8Ovr6+Pq1avIz89HSUlJi47t4+ODnTt34s8//2xwfUhICIqKirjhiu+bOHEiNm/ejJs3byInJwcCgQBeXl4wMjIS2qa8vBz5+flCS+3TwBtib2+P0tJSPHjwoNE8Tk5OePr0KdasWdPM0gr7xz/+gfDwcMTGxiIlJQXz5s1DQUFBvXxRUVEYNWqUSMdo6tguLi6YM2cOoqOjkZycjBkzZkBXVxcuLi5cPjs7Oxw5cgSDBg2CkpISJCQkMHz4cBw6dEio0ndwcICVlRXGjx+Py5cvIzs7G7GxsVixYsUH3+TSlDdv3mDRokUQCATIyclBTEwM4uPjYWJi0uR2x48fx759+5CWlgY/Pz/cunWLawS5urpCXV0dLi4uiIqKQlZWFgQCARYvXow//vgDwLvv9N27d5GamoqioiJUVlZCQ0MD8vLy3IPUXrx4AeDD1w8hhJCPx8fc5mlPfD4f7u7uWL58OSIiIvDgwQN4enpCQkLigw8fras5daoosrKy8PXXXyMuLg45OTm4fPkyHj9+/MH6/+eff8apU6fw6NEjeHl5oaSkhJuy7OXlhWfPnmHatGmIj49HRkYGLl26hM8//5zr4NDX18fNmzeRnZ2NoqIi1NTUQE9PDzweD2fPnsXTp09RWloKPp+PZcuWYcmSJdi/fz8yMjJw584dbN++vd6D0cnHhTpBCCEAgNmzZ2PPnj0IDg6GqakpbG1tERISwk0l4fP5+OGHH2BhYYEhQ4YgOzsb58+fh4TEu5+RjRs3Ijw8HD169ODutjeXk5MTDAwMEBAQ0OB6eXn5Jt8Z7+joiN9//x3Ozs4wMjKCu7s7jI2NcfnyZaFe/osXL0JbW1toGTZsWKP77dq1KyZMmNDk3Fsejwd1dfUWzXGt69tvv8XgwYPh6OgIOzs7aGlpCb26DgDevn2L06dPY86cOSIdoynBwcEwNzfHuHHjYGVlBcYYzp8/LzTs1dbWFtXV1UKvg7Wzs6uXxuPxcP78eQwfPhyff/45jIyMMHXqVOTk5DQ5dPdDJCUlUVxcjJkzZ8LIyAiTJ0/G6NGjsXr16ia3W716NUJDQzFgwAAcOHAAR44c4Ua4KCgo4Pr16+jZsyf+/e9/w8TEhJvWVHsXa86cOejTpw8sLCzQrVs3xMTEQEpKCtu2bcPOnTuho6PDdRZ96PohhBDy8fiY2zztbdOmTbCyssK4cePg4OAAGxsb7tXBzdWcOlUUCgoKePToET777DMYGRlh7ty58PLywrx585rcLjAwEIGBgRg4cCCio6MRFhbGjaTV0dFBTEwMqqurMWrUKJiamsLHxweqqqrc33fZsmWQlJRE37590a1bN+Tm5kJXVxerV6+Gr68vNDU1uZsq/v7+WLlyJdatWwcTExM4OTnh3LlzVP9/5HisNSaJEUJIB3X37l188sknyMjIaPYDR1tbUFAQTp069ZcezEYIIYQQ8iGvX7+Grq4uNm7cCE9PT3GHQ0iboJEghBDShAEDBmD9+vXIysoSWwzS0tLYvn272I5PCCGEkI4pMTERR44c4aZy1D4npe60WEI6GhoJQgghhBBCCCGdUGJiImbPno3U1FTIyMjA3NwcmzZtgqmpqbhDI6TNUCcIIYQQQgghhBBCOgWaDkMIIYQQQgghhJBOgTpBCCGEEEIIIYQQ0ilQJwghhBBCCCGEEEI6BeoEIYQQQgghhBBCSKdAnSCEEEIIIYQQQgjpFKgThBBCCCGEEEIIIZ0CdYIQQgghhBBCCCGkU6BOEEIIIYQQQgghhHQK/wVwRyEsgT8UAgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model                       RMSE (MPa)       R²\n",
      "---------------------------------------------\n",
      "GPR                               4.48    0.933\n",
      "Optimized random forest           5.18    0.910\n",
      "Random forest                     5.80    0.887\n",
      "SVR                               5.88    0.884\n",
      "Optimized linear pipeline         6.51    0.858\n",
      "Optimized SVR pipeline            6.87    0.842\n",
      "KNN                               7.84    0.794\n",
      "Decision tree                     8.61    0.751\n",
      "Linear regression                10.97    0.596\n"
     ]
    }
   ],
   "source": [
    "names = list(results.keys())\n",
    "rmses = [results[n]['rmse'] for n in names]\n",
    "r2s   = [results[n]['r2']   for n in names]\n",
    "\n",
    "order   = np.argsort(rmses)\n",
    "names_s = [names[i] for i in order]\n",
    "rmses_s = [rmses[i] for i in order]\n",
    "r2s_s   = [r2s[i]   for i in order]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
    "\n",
    "ax1.barh(names_s, rmses_s, color='steelblue')\n",
    "ax1.set_xlabel('Test RMSE (MPa) - lower is better')\n",
    "ax1.set_title('Test RMSE by model')\n",
    "\n",
    "ax2.barh(names_s, r2s_s, color='teal')\n",
    "ax2.set_xlabel('Test R² - higher is better')\n",
    "ax2.set_title('Test R² by model')\n",
    "ax2.axvline(0, color='black', linewidth=0.8)\n",
    "\n",
    "plt.suptitle('Concrete compressive strength', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f'{\"Model\":<25} {\"RMSE (MPa)\":>12} {\"R²\":>8}')\n",
    "print('-' * 45)\n",
    "\n",
    "for n, r, r2 in zip(names_s, rmses_s, r2s_s):\n",
    "    print(f'{n:<25} {r:>12.2f} {r2:>8.3f}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv (3.12.3)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}