{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Support Vector Machines – Applied\n",
    "\n",
    "Applied excercises from **Chapter 9** of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import patsy as pt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import cross_val_score \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.datasets import make_moons, make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Generate a simulated two-class data set with 100 observations and two features in which there is a visible but non-linear separation between the two classes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate noisy moon shaped data\n",
    "n_samples = 100\n",
    "noise     = .3\n",
    "X_train, y_train = make_moons(n_samples=n_samples, noise=noise, random_state=0)\n",
    "X_test, y_test = make_moons(n_samples=n_samples, noise=noise, random_state=1)\n",
    "\n",
    "# Scale data\n",
    "X_train = preprocessing.scale(X_train)\n",
    "X_test = preprocessing.scale(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data\n",
    "df = pd.concat([pd.DataFrame(data=X_train, columns=['x1', 'x2']), pd.Series(y_train, name='y')], axis=1)\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.scatterplot(x='x1', y='x2', hue='y', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ... Show that in this setting, a support vector machine with a polynomial kernel (with degree greater than 1) or a radial kernel will outperform a support vector classifier on the training data. \n",
    "\n",
    "### Which technique performs best on the test data? Make plots and report training and test error rates in order to back up your assertions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_clf(model, df, grid_range, show_contours=False, show_support_vectors=False):\n",
    "    # Decision boundary plot\n",
    "    \n",
    "    # Get grid of values in given range\n",
    "    x1 = grid_range\n",
    "    x2 = grid_range\n",
    "    xx1, xx2 = np.meshgrid(x1, x2, sparse=False)\n",
    "    Xgrid = np.stack((xx1.flatten(), xx2.flatten())).T\n",
    "    \n",
    "    # Get decision boundary values for plot grid\n",
    "    decision_boundary      = model.predict(Xgrid)\n",
    "    decision_boundary_grid = decision_boundary.reshape(len(x2), len(x1))\n",
    "    \n",
    "    # Get decision function values for plot grid\n",
    "    decision_function      = model.decision_function(Xgrid)\n",
    "    decision_function_grid = decision_function.reshape(len(x2), len(x1))\n",
    "    \n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    if show_contours:\n",
    "        plt.contourf(x1, x2, decision_function_grid);\n",
    "    plt.contour(x1, x2, decision_boundary_grid);\n",
    "    \n",
    "    sns.scatterplot(x='x1', y='x2', hue='y', data=df)\n",
    "    if show_support_vectors:\n",
    "        sns.scatterplot(x=model.support_vectors_[:,0], y=model.support_vectors_[:,1], color='red', marker='+', s=500)\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.85\n",
      "Test accuracy    : 0.83\n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='linear', gamma=1, C=1, random_state=0, probability=True).fit(X_train, y_train)\n",
    "\n",
    "plot_clf(model, df, np.arange(-3, 3, .1), show_contours=True)\n",
    "\n",
    "print(f'Training accuracy: {model.score(X_train, y_train)}')\n",
    "print(f'Test accuracy    : {model.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Polynomial kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.85\n",
      "Test accuracy    : 0.87\n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='poly', degree=3, gamma=1, C=1, random_state=0, probability=True).fit(X_train, y_train)\n",
    "\n",
    "plot_clf(model, df, np.arange(-3, 3, .1), show_contours=True)\n",
    "\n",
    "print(f'Training accuracy: {model.score(X_train, y_train)}')\n",
    "print(f'Test accuracy    : {model.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Radial kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.93\n",
      "Test accuracy    : 0.91\n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf', gamma=1, C=1, random_state=0, probability=True).fit(X_train, y_train)\n",
    "\n",
    "plot_clf(model, df, np.arange(-3, 3, .1), show_contours=True)\n",
    "\n",
    "print(f'Training accuracy: {model.score(X_train, y_train)}')\n",
    "print(f'Test accuracy    : {model.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment** In this setting an SVM with a radial kernel outperforms both linear and polynomial kernel models. The linear kernel model is the least effective because the data is not linearly separable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. We have seen that we can fit an SVM with a non-linear kernel in order to perform classification using a non-linear decision boundary. We will now see that we can also obtain a non-linear decision boundary by performing logistic regression using non-linear transformations of the features.\n",
    "\n",
    "(a) Generate a data set with n = 500 and p = 2, such that the observations belong to two classes with a quadratic decision boundary between them. For instance, you can do this as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "x1 = np.random.uniform(0, 1, 500) - 0.5\n",
    "x2 = np.random.uniform(0, 1, 500) - 0.5\n",
    "y = 1*(x1**2 - x2**2 > 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) Plot the observations, colored according to their class labels. Your plot should display X1 on the x-axis, and X2 on the y- axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data\n",
    "df = pd.DataFrame({'x1':x1, 'x2':x2, 'y':y})\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.scatterplot(x='x1', y='x2', hue='y', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) Fit a logistic regression model to the data, using X1 and X2 as predictors.\n",
    "\n",
    "(d) Apply this model to the training data in order to obtain a predicted class label for each training observation. Plot the observations, colored according to the predicted class labels. The decision boundary should be linear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-process data with only linear features\n",
    "f = 'y ~ x1 + x2'\n",
    "y, X = pt.dmatrices(f, df)\n",
    "y = np.ravel(y)\n",
    "\n",
    "train = np.random.random(len(y)) > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.5984555984555985\n",
      "Test accuracy    : 0.5228215767634855\n"
     ]
    }
   ],
   "source": [
    "# Fit model on training set\n",
    "model = LogisticRegression().fit(X[train], y[train])\n",
    "# Predict\n",
    "y_hat = model.predict(X[train])\n",
    "\n",
    "# Plot data\n",
    "plot_df = pd.DataFrame({'x1':X[train][:,1], 'x2':X[train][:,2], 'y_hat':y_hat})\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.scatterplot(x='x1', y='x2', hue='y_hat', data=plot_df)\n",
    "plt.show();\n",
    "\n",
    "print(f'Training accuracy: {model.score(X[train], y[train])}')\n",
    "print(f'Test accuracy    : {model.score(X[~train], y[~train])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(e) Now fit a logistic regression model to the data using non-linear functions of X1 and X2 as predictors (e.g. X12, X1 ×X2, log(X2), and so forth).\n",
    "\n",
    "(f) Apply this model to the training data in order to obtain a pre- dicted class label for each training observation. Plot the ob- servations, colored according to the predicted class labels. The decision boundary should be obviously non-linear. If it is not, then repeat (a)-(e) until you come up with an example in which the predicted class labels are obviously non-linear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.8957528957528957\n",
      "Test accuracy    : 0.8506224066390041\n"
     ]
    }
   ],
   "source": [
    "# Pre-process data with quadratic features\n",
    "f = 'y ~ x1 + x2 + np.power(x1, 2) + np.power(x2, 2)'\n",
    "y, X = pt.dmatrices(f, df)\n",
    "y = np.ravel(y)\n",
    "\n",
    "# Fit model on training set\n",
    "model = LogisticRegression().fit(X[train], y[train])\n",
    "# Predict\n",
    "y_hat = model.predict(X[train])\n",
    "\n",
    "# Plot data\n",
    "plot_df = pd.DataFrame({'x1':X[train][:,1], 'x2':X[train][:,2], 'y_hat':y_hat})\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.scatterplot(x='x1', y='x2', hue='y_hat', data=plot_df)\n",
    "plt.show();\n",
    "\n",
    "print(f'Training accuracy: {model.score(X[train], y[train])}')\n",
    "print(f'Test accuracy    : {model.score(X[~train], y[~train])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(g) Fit a support vector classifier to the data with X1 and X2 as predictors. Obtain a class prediction for each training observation. Plot the observations, colored according to the predicted class labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.6023166023166023\n",
      "Test accuracy    : 0.5186721991701245\n"
     ]
    }
   ],
   "source": [
    "f = 'y ~ x1 + x2 - 1'\n",
    "y, X = pt.dmatrices(f, df)\n",
    "y = np.ravel(y)\n",
    "\n",
    "model = svm.SVC(kernel='linear', gamma=1, C=1, random_state=0, probability=True).fit(X[train], y[train])\n",
    "\n",
    "plot_clf(model, df, np.arange(-.8, .9, .005), show_contours=True)\n",
    "\n",
    "print(f'Training accuracy: {model.score(X[train], y[train])}')\n",
    "print(f'Test accuracy    : {model.score(X[~train], y[~train])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(h) Fit a SVM using a non-linear kernel to the data. Obtain a class prediction for each training observation. Plot the observations, colored according to the predicted class labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.9420849420849421\n",
      "Test accuracy    : 0.9336099585062241\n"
     ]
    }
   ],
   "source": [
    "f = 'y ~ x1 + x2 - 1'\n",
    "y, X = pt.dmatrices(f, df)\n",
    "y = np.ravel(y)\n",
    "\n",
    "model = svm.SVC(kernel='rbf', gamma=1, C=1, random_state=0, probability=True).fit(X[train], y[train])\n",
    "\n",
    "plot_clf(model, df, np.arange(-.9, .9, .005), show_contours=True)\n",
    "\n",
    "print(f'Training accuracy: {model.score(X[train], y[train])}')\n",
    "print(f'Test accuracy    : {model.score(X[~train], y[~train])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(i) Comment on your results.\n",
    "\n",
    "Polynomial logistic regression and SVM with radial kernel achieve similar fits to the data. The SVM performs best on the test set with an accuracy of 93% compared to 85% from logistic regression. \n",
    "\n",
    "The linear support vector classifier achieves poor results because the seperation between classes is far from linear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. At the end of Section 9.6.1, it is claimed that in the case of data that is just barely linearly separable, a support vector classifier with a small value of cost that misclassifies a couple of training observations may perform better on test data than one with a huge value of cost that does not misclassify any training observations. You will now investigate this claim.\n",
    "\n",
    "(a) Generate two-class data with p = 2 in such a way that the classes are just barely linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate sample data\n",
    "\n",
    "X, _ = make_blobs(n_samples=200, centers=1, n_features=2, random_state=0)\n",
    "y = np.zeros(len(X))\n",
    "y[np.sum(X, axis=1) > 5] = 1\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=1)\n",
    "\n",
    "# Scale data\n",
    "X_train = preprocessing.scale(X_train)\n",
    "X_test = preprocessing.scale(X_test)\n",
    "\n",
    "# Plot data\n",
    "plot_df = pd.DataFrame({'x1':X_train[:,0], 'x2':X_train[:,1], 'y':y_train})\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.scatterplot(x='x1', y='x2', hue='y', data=plot_df)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) Compute the cross-validation error rates for support vector classifiers with a range of cost values. How many training errors are misclassified for each value of cost considered, and how does this relate to the cross-validation errors obtained?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) Generate an appropriate test data set, and compute the test errors corresponding to each of the values of cost considered. Which value of cost leads to the fewest test errors, and how does this compare to the values of cost that yield the fewest training errors and the fewest cross-validation errors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "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>Cost</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>test_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.530050</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.641589e-04</td>\n",
       "      <td>0.530050</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.154435e-02</td>\n",
       "      <td>0.928847</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.940376</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.641589e+01</td>\n",
       "      <td>0.969950</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.154435e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.641589e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.154435e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Cost  train_accuracy  test_accuracy\n",
       "0  1.000000e-05        0.530050           0.57\n",
       "1  4.641589e-04        0.530050           0.57\n",
       "2  2.154435e-02        0.928847           1.00\n",
       "3  1.000000e+00        0.940376           0.94\n",
       "4  4.641589e+01        0.969950           0.96\n",
       "5  2.154435e+03        1.000000           0.97\n",
       "6  1.000000e+05        1.000000           0.97\n",
       "7  4.641589e+06        1.000000           0.97\n",
       "8  2.154435e+08        1.000000           0.97\n",
       "9  1.000000e+10        1.000000           0.97"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "costs = np.logspace(-5, 10, 10)\n",
    "scores = []\n",
    "for i in costs:\n",
    "    # Get cv score \n",
    "    model = svm.SVC(kernel='linear', C=i, random_state=0)\n",
    "    train_score = np.mean(cross_val_score(model, X_train, y_train, cv=5))\n",
    "    # Get test score\n",
    "    model = svm.SVC(kernel='linear', C=i, random_state=0).fit(X_train, y_train)\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    scores += [[i, train_score, test_score]]\n",
    "\n",
    "columns=['Cost', 'train_accuracy', 'test_accuracy']\n",
    "results_df = pd.DataFrame(data=np.asarray(scores), columns=columns)\n",
    "\n",
    "display(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'accuracy')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df['log(Cost)'] = np.log(results_df['Cost'])\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.lineplot(x='log(Cost)', y='train_accuracy', data=results_df, label='train')\n",
    "sns.lineplot(x='log(Cost)', y='test_accuracy', data=results_df, label='test')\n",
    "plt.ylabel('accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(d) Discuss your results.\n",
    "\n",
    "**Comment** For high values of Cost the model achieves perfect training accuracy. The best test accuracy is also perfect but this is achieved for a lower cost value.\n",
    "\n",
    "This suggests that the model starts to overfit as cost becomes too high.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. In this problem, you will use support vector approaches in order to predict whether a given car gets high or low gas mileage based on the Auto data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(a) Create a binary variable that takes on a 1 for cars with gas mileage above the median, and a 0 for cars with gas mileage below the median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "      <th>mpg_above_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>amc rebel sst</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ford torino</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
       "0  18.0        8.0         307.0       130.0  3504.0          12.0  70.0   \n",
       "1  15.0        8.0         350.0       165.0  3693.0          11.5  70.0   \n",
       "2  18.0        8.0         318.0       150.0  3436.0          11.0  70.0   \n",
       "3  16.0        8.0         304.0       150.0  3433.0          12.0  70.0   \n",
       "4  17.0        8.0         302.0       140.0  3449.0          10.5  70.0   \n",
       "\n",
       "   origin                       name  mpg_above_median  \n",
       "0     1.0  chevrolet chevelle malibu               0.0  \n",
       "1     1.0          buick skylark 320               0.0  \n",
       "2     1.0         plymouth satellite               0.0  \n",
       "3     1.0              amc rebel sst               0.0  \n",
       "4     1.0                ford torino               0.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_df = pd.read_csv('./data/Auto.csv')\n",
    "\n",
    "# Remove observations with missing values\n",
    "auto_df = auto_df.drop(auto_df[auto_df.values == '?'].index)\n",
    "#auto_df = auto_df.reset_index()\n",
    "\n",
    "# convet quantitive values to floats\n",
    "#quants = ['cylinders', 'horsepower', 'weight', 'year']\n",
    "quants = ['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'year', 'origin']\n",
    "auto_df[quants] = auto_df[quants].astype(np.float64)\n",
    "\n",
    "auto_df['mpg_above_median'] = (auto_df['mpg'] > auto_df['mpg'].median()) *1.\n",
    "auto_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) Fit a support vector classifier to the data with various values of cost, in order to predict whether a car gets high or low gas mileage. Report the cross-validation errors associated with different values of this parameter. Comment on your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = 'mpg_above_median ~ cylinders + displacement + horsepower + weight + acceleration + year + C(origin)'\n",
    "y, X = pt.dmatrices(f, auto_df)\n",
    "\n",
    "# Scale data\n",
    "X = preprocessing.scale(X)\n",
    "y = np.ravel(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "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>Cost</th>\n",
       "      <th>CV_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.787692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.787692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.787692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.787692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.787692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000695</td>\n",
       "      <td>0.818205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.001624</td>\n",
       "      <td>0.877179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.003793</td>\n",
       "      <td>0.882308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.008859</td>\n",
       "      <td>0.902821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.020691</td>\n",
       "      <td>0.895385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.048329</td>\n",
       "      <td>0.887949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.112884</td>\n",
       "      <td>0.870385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.263665</td>\n",
       "      <td>0.862821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.615848</td>\n",
       "      <td>0.842821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.438450</td>\n",
       "      <td>0.855577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.359818</td>\n",
       "      <td>0.855641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.847600</td>\n",
       "      <td>0.855641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.329807</td>\n",
       "      <td>0.853077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>42.813324</td>\n",
       "      <td>0.853077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.853077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Cost  CV_accuracy\n",
       "0     0.000010     0.787692\n",
       "1     0.000023     0.787692\n",
       "2     0.000055     0.787692\n",
       "3     0.000127     0.787692\n",
       "4     0.000298     0.787692\n",
       "5     0.000695     0.818205\n",
       "6     0.001624     0.877179\n",
       "7     0.003793     0.882308\n",
       "8     0.008859     0.902821\n",
       "9     0.020691     0.895385\n",
       "10    0.048329     0.887949\n",
       "11    0.112884     0.870385\n",
       "12    0.263665     0.862821\n",
       "13    0.615848     0.842821\n",
       "14    1.438450     0.855577\n",
       "15    3.359818     0.855641\n",
       "16    7.847600     0.855641\n",
       "17   18.329807     0.853077\n",
       "18   42.813324     0.853077\n",
       "19  100.000000     0.853077"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "costs = np.logspace(-5, 2, 20)\n",
    "scores = []\n",
    "for i in costs:\n",
    "    # Get cv score \n",
    "    model = svm.SVC(kernel='linear', C=i, random_state=0)\n",
    "    score = np.mean(cross_val_score(model, preprocessing.scale(X), y, cv=5))\n",
    "    scores += [[i, score]]\n",
    "    #print(f'progress: {list(costs).index(i)} of {len(costs)}')\n",
    "\n",
    "columns=['Cost', 'CV_accuracy']\n",
    "results_df = pd.DataFrame(data=np.asarray(scores), columns=columns)\n",
    "\n",
    "display(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df['log(Cost)'] = np.log(results_df['Cost'])\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.lineplot(x='log(Cost)', y='CV_accuracy', data=results_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) Now repeat (b), this time using SVMs with radial and polynomial basis kernels, with different values of gamma and degree and cost. Comment on your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=2000, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'gamma': array([1.00000e-05, 7.74264e-05, 5.99484e-04, 4.64159e-03, 3.59381e-02,\n",
       "       2.78256e-01, 2.15443e+00, 1.66810e+01, 1.29155e+02, 1.00000e+03]), 'C': array([1.00000e-05, 7.74264e-05, 5.99484e-04, 4.64159e-03, 3.59381e-02,\n",
       "       2.78256e-01, 2.15443e+00, 1.66810e+01, 1.29155e+02, 1.00000e+03]), 'kernel': ['rbf', 'poly'], 'degree': [3, 5, 7, 9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid search for best classifier\n",
    "# https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py\n",
    "# ----------------------------------------------------------------\n",
    "\n",
    "C_range     = np.logspace(-5, 3, 10)\n",
    "gamma_range = np.logspace(-5, 3, 10)\n",
    "kernels     = ['rbf', 'poly']\n",
    "degrees     = [3, 5, 7, 9]     # Using only odd values, because I noticed earlier evens are slow!\n",
    "param_grid  = dict(gamma=gamma_range, C=C_range, kernel=kernels, degree=degrees)\n",
    "rbf_grid = GridSearchCV(svm.SVC(cache_size=2000), param_grid=param_grid, cv=5, \n",
    "                        scoring='accuracy', return_train_score=True)\n",
    "rbf_grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_degree</th>\n",
       "      <th>param_gamma</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>0.001242</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>2.15443</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00464159</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 2.154434690031882, 'degree': 3, 'gamma':...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903061</td>\n",
       "      <td>0.028045</td>\n",
       "      <td>1</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.915180</td>\n",
       "      <td>0.005520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.001252</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>2.15443</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00464159</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 2.154434690031882, 'degree': 5, 'gamma':...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903061</td>\n",
       "      <td>0.028045</td>\n",
       "      <td>1</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.915180</td>\n",
       "      <td>0.005520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>0.001246</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>2.15443</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00464159</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 2.154434690031882, 'degree': 7, 'gamma':...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903061</td>\n",
       "      <td>0.028045</td>\n",
       "      <td>1</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.915180</td>\n",
       "      <td>0.005520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>0.001248</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>2.15443</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00464159</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 2.154434690031882, 'degree': 9, 'gamma':...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903061</td>\n",
       "      <td>0.028045</td>\n",
       "      <td>1</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.915180</td>\n",
       "      <td>0.005520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>0.002271</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>3</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.03593813663804626, 'degree': 3, 'gamma...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900510</td>\n",
       "      <td>0.033323</td>\n",
       "      <td>5</td>\n",
       "      <td>0.900641</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.910701</td>\n",
       "      <td>0.005557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>0.001312</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 3, 'gamma'...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900510</td>\n",
       "      <td>0.037041</td>\n",
       "      <td>5</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.920382</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.913266</td>\n",
       "      <td>0.004214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>0.001307</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 5, 'gamma'...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900510</td>\n",
       "      <td>0.037041</td>\n",
       "      <td>5</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.920382</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.913266</td>\n",
       "      <td>0.004214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 7, 'gamma'...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900510</td>\n",
       "      <td>0.037041</td>\n",
       "      <td>5</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.920382</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.913266</td>\n",
       "      <td>0.004214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0.002348</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>9</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.03593813663804626, 'degree': 9, 'gamma...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900510</td>\n",
       "      <td>0.033323</td>\n",
       "      <td>5</td>\n",
       "      <td>0.900641</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.910701</td>\n",
       "      <td>0.005557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>0.001374</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 9, 'gamma'...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900510</td>\n",
       "      <td>0.037041</td>\n",
       "      <td>5</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.920382</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.913266</td>\n",
       "      <td>0.004214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>0.002316</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>5</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.03593813663804626, 'degree': 5, 'gamma...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900510</td>\n",
       "      <td>0.033323</td>\n",
       "      <td>5</td>\n",
       "      <td>0.900641</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.910701</td>\n",
       "      <td>0.005557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>0.002351</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>7</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.03593813663804626, 'degree': 7, 'gamma...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900510</td>\n",
       "      <td>0.033323</td>\n",
       "      <td>5</td>\n",
       "      <td>0.900641</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.910701</td>\n",
       "      <td>0.005557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>0.001422</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>129.155</td>\n",
       "      <td>9</td>\n",
       "      <td>7.74264e-05</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 129.15496650148827, 'degree': 9, 'gamma'...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.914539</td>\n",
       "      <td>0.005496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>0.001270</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>16.681</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000599484</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 16.681005372000556, 'degree': 9, 'gamma'...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.915180</td>\n",
       "      <td>0.005520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>129.155</td>\n",
       "      <td>3</td>\n",
       "      <td>7.74264e-05</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 129.15496650148827, 'degree': 3, 'gamma'...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.914539</td>\n",
       "      <td>0.005496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>0.001265</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>16.681</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000599484</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 16.681005372000556, 'degree': 7, 'gamma'...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.915180</td>\n",
       "      <td>0.005520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1000.0, 'degree': 7, 'gamma': 1e-05, 'ke...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.914539</td>\n",
       "      <td>0.005496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>0.001297</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1000.0, 'degree': 5, 'gamma': 1e-05, 'ke...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.914539</td>\n",
       "      <td>0.005496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>129.155</td>\n",
       "      <td>7</td>\n",
       "      <td>7.74264e-05</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 129.15496650148827, 'degree': 7, 'gamma'...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.914539</td>\n",
       "      <td>0.005496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>0.001266</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1000.0, 'degree': 3, 'gamma': 1e-05, 'ke...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.914539</td>\n",
       "      <td>0.005496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>0.001257</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1000</td>\n",
       "      <td>9</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1000.0, 'degree': 9, 'gamma': 1e-05, 'ke...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.914539</td>\n",
       "      <td>0.005496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>16.681</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000599484</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 16.681005372000556, 'degree': 5, 'gamma'...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.915180</td>\n",
       "      <td>0.005520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>0.001275</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>129.155</td>\n",
       "      <td>5</td>\n",
       "      <td>7.74264e-05</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 129.15496650148827, 'degree': 5, 'gamma'...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.914539</td>\n",
       "      <td>0.005496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>0.001268</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>16.681</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000599484</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 16.681005372000556, 'degree': 3, 'gamma'...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.023165</td>\n",
       "      <td>13</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.917197</td>\n",
       "      <td>0.915180</td>\n",
       "      <td>0.005520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>0.001396</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000411</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>5</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 5, 'gamma'...</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.895408</td>\n",
       "      <td>0.022086</td>\n",
       "      <td>25</td>\n",
       "      <td>0.919872</td>\n",
       "      <td>0.920382</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.918369</td>\n",
       "      <td>0.003776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>0.001382</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>3</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 3, 'gamma'...</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.895408</td>\n",
       "      <td>0.022086</td>\n",
       "      <td>25</td>\n",
       "      <td>0.919872</td>\n",
       "      <td>0.920382</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.918369</td>\n",
       "      <td>0.003776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>0.001389</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>7</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 7, 'gamma'...</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.895408</td>\n",
       "      <td>0.022086</td>\n",
       "      <td>25</td>\n",
       "      <td>0.919872</td>\n",
       "      <td>0.920382</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.918369</td>\n",
       "      <td>0.003776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>0.001390</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>9</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 9, 'gamma'...</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.895408</td>\n",
       "      <td>0.022086</td>\n",
       "      <td>25</td>\n",
       "      <td>0.919872</td>\n",
       "      <td>0.920382</td>\n",
       "      <td>0.923567</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.918369</td>\n",
       "      <td>0.003776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.002605</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000671</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.00464159</td>\n",
       "      <td>3</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.004641588833612777, 'degree': 3, 'gamm...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.887755</td>\n",
       "      <td>0.049905</td>\n",
       "      <td>29</td>\n",
       "      <td>0.894231</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.898089</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.904961</td>\n",
       "      <td>0.007562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000599484</td>\n",
       "      <td>5</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.0005994842503189409, 'degree': 5, 'gam...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.887755</td>\n",
       "      <td>0.049905</td>\n",
       "      <td>29</td>\n",
       "      <td>0.894231</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.898089</td>\n",
       "      <td>0.910828</td>\n",
       "      <td>0.904961</td>\n",
       "      <td>0.007562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.002456</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>9</td>\n",
       "      <td>129.155</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1e-05, 'degree': 9, 'gamma': 129.1549665...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.505102</td>\n",
       "      <td>0.006275</td>\n",
       "      <td>721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>0.003039</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>129.155</td>\n",
       "      <td>9</td>\n",
       "      <td>129.155</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 129.15496650148827, 'degree': 9, 'gamma'...</td>\n",
       "      <td>0.5250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.505102</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0.002508</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>7.74264e-05</td>\n",
       "      <td>7</td>\n",
       "      <td>129.155</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 7.742636826811278e-05, 'degree': 7, 'gam...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.505102</td>\n",
       "      <td>0.006275</td>\n",
       "      <td>721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0.002479</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>3</td>\n",
       "      <td>129.155</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 3, 'gamma'...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.505102</td>\n",
       "      <td>0.006275</td>\n",
       "      <td>721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>0.001888</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>16.681</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 16.681005372000556, 'degree': 9, 'gamma'...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.505102</td>\n",
       "      <td>0.006275</td>\n",
       "      <td>721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>0.003163</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>129.155</td>\n",
       "      <td>7</td>\n",
       "      <td>129.155</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 129.15496650148827, 'degree': 7, 'gamma'...</td>\n",
       "      <td>0.5250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.505102</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0.001855</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.00464159</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.004641588833612777, 'degree': 3, 'gamm...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>0.002007</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.03593813663804626, 'degree': 9, 'gamma...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.00464159</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.004641588833612777, 'degree': 5, 'gamm...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0.001931</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.00464159</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.004641588833612777, 'degree': 7, 'gamm...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>0.001850</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.03593813663804626, 'degree': 5, 'gamma...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>0.001872</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 9, 'gamma'...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>0.001870</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 7, 'gamma'...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.001847</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1e-05, 'degree': 9, 'gamma': 1000.0, 'ke...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>0.001872</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 3, 'gamma'...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.001855</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>7.74264e-05</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 7.742636826811278e-05, 'degree': 3, 'gam...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>0.001866</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000599484</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.0005994842503189409, 'degree': 7, 'gam...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>0.001847</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>7.74264e-05</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 7.742636826811278e-05, 'degree': 5, 'gam...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.002360</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>0.000746</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1e-05, 'degree': 3, 'gamma': 1000.0, 'ke...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>0.001889</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.278256</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.2782559402207126, 'degree': 5, 'gamma'...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>0.002340</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.03593813663804626, 'degree': 3, 'gamma...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.001853</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.00464159</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.004641588833612777, 'degree': 9, 'gamm...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0359381</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.03593813663804626, 'degree': 7, 'gamma...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.001926</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1e-05, 'degree': 5, 'gamma': 1000.0, 'ke...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1e-05, 'degree': 7, 'gamma': 1000.0, 'ke...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000599484</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.0005994842503189409, 'degree': 3, 'gam...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.001881</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>7.74264e-05</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 7.742636826811278e-05, 'degree': 7, 'gam...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.001846</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>7.74264e-05</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 7.742636826811278e-05, 'degree': 9, 'gam...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0.001850</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000599484</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.0005994842503189409, 'degree': 9, 'gam...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000599484</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.0005994842503189409, 'degree': 5, 'gam...</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "486       0.001242      0.000037         0.000400        0.000011   \n",
       "506       0.001252      0.000034         0.000397        0.000007   \n",
       "526       0.001246      0.000037         0.000397        0.000006   \n",
       "546       0.001248      0.000037         0.000395        0.000007   \n",
       "330       0.002271      0.000100         0.000599        0.000010   \n",
       "408       0.001312      0.000038         0.000409        0.000010   \n",
       "428       0.001307      0.000042         0.000407        0.000011   \n",
       "448       0.001330      0.000028         0.000407        0.000010   \n",
       "390       0.002348      0.000127         0.000616        0.000029   \n",
       "468       0.001374      0.000165         0.000407        0.000010   \n",
       "350       0.002316      0.000052         0.000604        0.000010   \n",
       "370       0.002351      0.000176         0.000696        0.000184   \n",
       "702       0.001422      0.000192         0.000449        0.000062   \n",
       "624       0.001270      0.000029         0.000403        0.000007   \n",
       "642       0.001263      0.000030         0.000397        0.000008   \n",
       "604       0.001265      0.000031         0.000395        0.000007   \n",
       "760       0.001263      0.000026         0.000398        0.000011   \n",
       "740       0.001297      0.000044         0.000407        0.000015   \n",
       "682       0.001276      0.000050         0.000399        0.000009   \n",
       "720       0.001266      0.000035         0.000408        0.000012   \n",
       "780       0.001257      0.000020         0.000394        0.000007   \n",
       "584       0.001263      0.000024         0.000396        0.000010   \n",
       "662       0.001275      0.000044         0.000401        0.000015   \n",
       "564       0.001268      0.000027         0.000432        0.000061   \n",
       "430       0.001396      0.000041         0.000411        0.000010   \n",
       "410       0.001382      0.000046         0.000408        0.000011   \n",
       "450       0.001389      0.000064         0.000406        0.000008   \n",
       "470       0.001390      0.000047         0.000407        0.000005   \n",
       "250       0.002605      0.000087         0.000671        0.000012   \n",
       "190       0.002529      0.000011         0.000657        0.000004   \n",
       "..             ...           ...              ...             ...   \n",
       "76        0.002456      0.000026         0.000636        0.000013   \n",
       "716       0.003039      0.000187         0.000644        0.000008   \n",
       "136       0.002508      0.000059         0.000649        0.000012   \n",
       "416       0.002479      0.000040         0.000642        0.000009   \n",
       "638       0.001888      0.000037         0.000503        0.000008   \n",
       "696       0.003163      0.000313         0.000679        0.000058   \n",
       "258       0.001855      0.000016         0.000499        0.000003   \n",
       "398       0.002007      0.000159         0.000569        0.000112   \n",
       "278       0.001857      0.000014         0.000500        0.000003   \n",
       "298       0.001931      0.000082         0.000525        0.000026   \n",
       "358       0.001850      0.000014         0.000507        0.000010   \n",
       "478       0.001872      0.000028         0.000504        0.000008   \n",
       "458       0.001870      0.000012         0.000505        0.000009   \n",
       "78        0.001847      0.000011         0.000500        0.000003   \n",
       "418       0.001872      0.000025         0.000507        0.000014   \n",
       "98        0.001855      0.000032         0.000502        0.000006   \n",
       "218       0.001866      0.000018         0.000517        0.000030   \n",
       "118       0.001847      0.000008         0.000501        0.000007   \n",
       "18        0.002360      0.000248         0.000746        0.000191   \n",
       "438       0.001889      0.000029         0.000511        0.000015   \n",
       "338       0.002340      0.000443         0.000928        0.000450   \n",
       "318       0.001853      0.000016         0.000504        0.000009   \n",
       "378       0.001885      0.000015         0.000503        0.000005   \n",
       "38        0.001926      0.000097         0.000523        0.000023   \n",
       "58        0.001898      0.000036         0.000534        0.000045   \n",
       "178       0.001868      0.000021         0.000510        0.000010   \n",
       "138       0.001881      0.000021         0.000504        0.000011   \n",
       "158       0.001846      0.000012         0.000504        0.000011   \n",
       "238       0.001850      0.000004         0.000502        0.000008   \n",
       "198       0.001851      0.000013         0.000510        0.000019   \n",
       "\n",
       "         param_C param_degree  param_gamma param_kernel  \\\n",
       "486      2.15443            3   0.00464159          rbf   \n",
       "506      2.15443            5   0.00464159          rbf   \n",
       "526      2.15443            7   0.00464159          rbf   \n",
       "546      2.15443            9   0.00464159          rbf   \n",
       "330    0.0359381            3     0.278256          rbf   \n",
       "408     0.278256            3    0.0359381          rbf   \n",
       "428     0.278256            5    0.0359381          rbf   \n",
       "448     0.278256            7    0.0359381          rbf   \n",
       "390    0.0359381            9     0.278256          rbf   \n",
       "468     0.278256            9    0.0359381          rbf   \n",
       "350    0.0359381            5     0.278256          rbf   \n",
       "370    0.0359381            7     0.278256          rbf   \n",
       "702      129.155            9  7.74264e-05          rbf   \n",
       "624       16.681            9  0.000599484          rbf   \n",
       "642      129.155            3  7.74264e-05          rbf   \n",
       "604       16.681            7  0.000599484          rbf   \n",
       "760         1000            7        1e-05          rbf   \n",
       "740         1000            5        1e-05          rbf   \n",
       "682      129.155            7  7.74264e-05          rbf   \n",
       "720         1000            3        1e-05          rbf   \n",
       "780         1000            9        1e-05          rbf   \n",
       "584       16.681            5  0.000599484          rbf   \n",
       "662      129.155            5  7.74264e-05          rbf   \n",
       "564       16.681            3  0.000599484          rbf   \n",
       "430     0.278256            5     0.278256          rbf   \n",
       "410     0.278256            3     0.278256          rbf   \n",
       "450     0.278256            7     0.278256          rbf   \n",
       "470     0.278256            9     0.278256          rbf   \n",
       "250   0.00464159            3     0.278256          rbf   \n",
       "190  0.000599484            5     0.278256          rbf   \n",
       "..           ...          ...          ...          ...   \n",
       "76         1e-05            9      129.155          rbf   \n",
       "716      129.155            9      129.155          rbf   \n",
       "136  7.74264e-05            7      129.155          rbf   \n",
       "416     0.278256            3      129.155          rbf   \n",
       "638       16.681            9         1000          rbf   \n",
       "696      129.155            7      129.155          rbf   \n",
       "258   0.00464159            3         1000          rbf   \n",
       "398    0.0359381            9         1000          rbf   \n",
       "278   0.00464159            5         1000          rbf   \n",
       "298   0.00464159            7         1000          rbf   \n",
       "358    0.0359381            5         1000          rbf   \n",
       "478     0.278256            9         1000          rbf   \n",
       "458     0.278256            7         1000          rbf   \n",
       "78         1e-05            9         1000          rbf   \n",
       "418     0.278256            3         1000          rbf   \n",
       "98   7.74264e-05            3         1000          rbf   \n",
       "218  0.000599484            7         1000          rbf   \n",
       "118  7.74264e-05            5         1000          rbf   \n",
       "18         1e-05            3         1000          rbf   \n",
       "438     0.278256            5         1000          rbf   \n",
       "338    0.0359381            3         1000          rbf   \n",
       "318   0.00464159            9         1000          rbf   \n",
       "378    0.0359381            7         1000          rbf   \n",
       "38         1e-05            5         1000          rbf   \n",
       "58         1e-05            7         1000          rbf   \n",
       "178  0.000599484            3         1000          rbf   \n",
       "138  7.74264e-05            7         1000          rbf   \n",
       "158  7.74264e-05            9         1000          rbf   \n",
       "238  0.000599484            9         1000          rbf   \n",
       "198  0.000599484            5         1000          rbf   \n",
       "\n",
       "                                                params  split0_test_score  \\\n",
       "486  {'C': 2.154434690031882, 'degree': 3, 'gamma':...             0.9375   \n",
       "506  {'C': 2.154434690031882, 'degree': 5, 'gamma':...             0.9375   \n",
       "526  {'C': 2.154434690031882, 'degree': 7, 'gamma':...             0.9375   \n",
       "546  {'C': 2.154434690031882, 'degree': 9, 'gamma':...             0.9375   \n",
       "330  {'C': 0.03593813663804626, 'degree': 3, 'gamma...             0.9500   \n",
       "408  {'C': 0.2782559402207126, 'degree': 3, 'gamma'...             0.9500   \n",
       "428  {'C': 0.2782559402207126, 'degree': 5, 'gamma'...             0.9500   \n",
       "448  {'C': 0.2782559402207126, 'degree': 7, 'gamma'...             0.9500   \n",
       "390  {'C': 0.03593813663804626, 'degree': 9, 'gamma...             0.9500   \n",
       "468  {'C': 0.2782559402207126, 'degree': 9, 'gamma'...             0.9500   \n",
       "350  {'C': 0.03593813663804626, 'degree': 5, 'gamma...             0.9500   \n",
       "370  {'C': 0.03593813663804626, 'degree': 7, 'gamma...             0.9500   \n",
       "702  {'C': 129.15496650148827, 'degree': 9, 'gamma'...             0.9125   \n",
       "624  {'C': 16.681005372000556, 'degree': 9, 'gamma'...             0.9125   \n",
       "642  {'C': 129.15496650148827, 'degree': 3, 'gamma'...             0.9125   \n",
       "604  {'C': 16.681005372000556, 'degree': 7, 'gamma'...             0.9125   \n",
       "760  {'C': 1000.0, 'degree': 7, 'gamma': 1e-05, 'ke...             0.9125   \n",
       "740  {'C': 1000.0, 'degree': 5, 'gamma': 1e-05, 'ke...             0.9125   \n",
       "682  {'C': 129.15496650148827, 'degree': 7, 'gamma'...             0.9125   \n",
       "720  {'C': 1000.0, 'degree': 3, 'gamma': 1e-05, 'ke...             0.9125   \n",
       "780  {'C': 1000.0, 'degree': 9, 'gamma': 1e-05, 'ke...             0.9125   \n",
       "584  {'C': 16.681005372000556, 'degree': 5, 'gamma'...             0.9125   \n",
       "662  {'C': 129.15496650148827, 'degree': 5, 'gamma'...             0.9125   \n",
       "564  {'C': 16.681005372000556, 'degree': 3, 'gamma'...             0.9125   \n",
       "430  {'C': 0.2782559402207126, 'degree': 5, 'gamma'...             0.9000   \n",
       "410  {'C': 0.2782559402207126, 'degree': 3, 'gamma'...             0.9000   \n",
       "450  {'C': 0.2782559402207126, 'degree': 7, 'gamma'...             0.9000   \n",
       "470  {'C': 0.2782559402207126, 'degree': 9, 'gamma'...             0.9000   \n",
       "250  {'C': 0.004641588833612777, 'degree': 3, 'gamm...             0.9375   \n",
       "190  {'C': 0.0005994842503189409, 'degree': 5, 'gam...             0.9375   \n",
       "..                                                 ...                ...   \n",
       "76   {'C': 1e-05, 'degree': 9, 'gamma': 129.1549665...             0.5000   \n",
       "716  {'C': 129.15496650148827, 'degree': 9, 'gamma'...             0.5250   \n",
       "136  {'C': 7.742636826811278e-05, 'degree': 7, 'gam...             0.5000   \n",
       "416  {'C': 0.2782559402207126, 'degree': 3, 'gamma'...             0.5000   \n",
       "638  {'C': 16.681005372000556, 'degree': 9, 'gamma'...             0.5000   \n",
       "696  {'C': 129.15496650148827, 'degree': 7, 'gamma'...             0.5250   \n",
       "258  {'C': 0.004641588833612777, 'degree': 3, 'gamm...             0.5000   \n",
       "398  {'C': 0.03593813663804626, 'degree': 9, 'gamma...             0.5000   \n",
       "278  {'C': 0.004641588833612777, 'degree': 5, 'gamm...             0.5000   \n",
       "298  {'C': 0.004641588833612777, 'degree': 7, 'gamm...             0.5000   \n",
       "358  {'C': 0.03593813663804626, 'degree': 5, 'gamma...             0.5000   \n",
       "478  {'C': 0.2782559402207126, 'degree': 9, 'gamma'...             0.5000   \n",
       "458  {'C': 0.2782559402207126, 'degree': 7, 'gamma'...             0.5000   \n",
       "78   {'C': 1e-05, 'degree': 9, 'gamma': 1000.0, 'ke...             0.5000   \n",
       "418  {'C': 0.2782559402207126, 'degree': 3, 'gamma'...             0.5000   \n",
       "98   {'C': 7.742636826811278e-05, 'degree': 3, 'gam...             0.5000   \n",
       "218  {'C': 0.0005994842503189409, 'degree': 7, 'gam...             0.5000   \n",
       "118  {'C': 7.742636826811278e-05, 'degree': 5, 'gam...             0.5000   \n",
       "18   {'C': 1e-05, 'degree': 3, 'gamma': 1000.0, 'ke...             0.5000   \n",
       "438  {'C': 0.2782559402207126, 'degree': 5, 'gamma'...             0.5000   \n",
       "338  {'C': 0.03593813663804626, 'degree': 3, 'gamma...             0.5000   \n",
       "318  {'C': 0.004641588833612777, 'degree': 9, 'gamm...             0.5000   \n",
       "378  {'C': 0.03593813663804626, 'degree': 7, 'gamma...             0.5000   \n",
       "38   {'C': 1e-05, 'degree': 5, 'gamma': 1000.0, 'ke...             0.5000   \n",
       "58   {'C': 1e-05, 'degree': 7, 'gamma': 1000.0, 'ke...             0.5000   \n",
       "178  {'C': 0.0005994842503189409, 'degree': 3, 'gam...             0.5000   \n",
       "138  {'C': 7.742636826811278e-05, 'degree': 7, 'gam...             0.5000   \n",
       "158  {'C': 7.742636826811278e-05, 'degree': 9, 'gam...             0.5000   \n",
       "238  {'C': 0.0005994842503189409, 'degree': 9, 'gam...             0.5000   \n",
       "198  {'C': 0.0005994842503189409, 'degree': 5, 'gam...             0.5000   \n",
       "\n",
       "          ...         mean_test_score  std_test_score  rank_test_score  \\\n",
       "486       ...                0.903061        0.028045                1   \n",
       "506       ...                0.903061        0.028045                1   \n",
       "526       ...                0.903061        0.028045                1   \n",
       "546       ...                0.903061        0.028045                1   \n",
       "330       ...                0.900510        0.033323                5   \n",
       "408       ...                0.900510        0.037041                5   \n",
       "428       ...                0.900510        0.037041                5   \n",
       "448       ...                0.900510        0.037041                5   \n",
       "390       ...                0.900510        0.033323                5   \n",
       "468       ...                0.900510        0.037041                5   \n",
       "350       ...                0.900510        0.033323                5   \n",
       "370       ...                0.900510        0.033323                5   \n",
       "702       ...                0.897959        0.023165               13   \n",
       "624       ...                0.897959        0.023165               13   \n",
       "642       ...                0.897959        0.023165               13   \n",
       "604       ...                0.897959        0.023165               13   \n",
       "760       ...                0.897959        0.023165               13   \n",
       "740       ...                0.897959        0.023165               13   \n",
       "682       ...                0.897959        0.023165               13   \n",
       "720       ...                0.897959        0.023165               13   \n",
       "780       ...                0.897959        0.023165               13   \n",
       "584       ...                0.897959        0.023165               13   \n",
       "662       ...                0.897959        0.023165               13   \n",
       "564       ...                0.897959        0.023165               13   \n",
       "430       ...                0.895408        0.022086               25   \n",
       "410       ...                0.895408        0.022086               25   \n",
       "450       ...                0.895408        0.022086               25   \n",
       "470       ...                0.895408        0.022086               25   \n",
       "250       ...                0.887755        0.049905               29   \n",
       "190       ...                0.887755        0.049905               29   \n",
       "..        ...                     ...             ...              ...   \n",
       "76        ...                0.505102        0.006275              721   \n",
       "716       ...                0.505102        0.012920              721   \n",
       "136       ...                0.505102        0.006275              721   \n",
       "416       ...                0.505102        0.006275              721   \n",
       "638       ...                0.505102        0.006275              721   \n",
       "696       ...                0.505102        0.012920              721   \n",
       "258       ...                0.500000        0.000000              777   \n",
       "398       ...                0.500000        0.000000              777   \n",
       "278       ...                0.500000        0.000000              777   \n",
       "298       ...                0.500000        0.000000              777   \n",
       "358       ...                0.500000        0.000000              777   \n",
       "478       ...                0.500000        0.000000              777   \n",
       "458       ...                0.500000        0.000000              777   \n",
       "78        ...                0.500000        0.000000              777   \n",
       "418       ...                0.500000        0.000000              777   \n",
       "98        ...                0.500000        0.000000              777   \n",
       "218       ...                0.500000        0.000000              777   \n",
       "118       ...                0.500000        0.000000              777   \n",
       "18        ...                0.500000        0.000000              777   \n",
       "438       ...                0.500000        0.000000              777   \n",
       "338       ...                0.500000        0.000000              777   \n",
       "318       ...                0.500000        0.000000              777   \n",
       "378       ...                0.500000        0.000000              777   \n",
       "38        ...                0.500000        0.000000              777   \n",
       "58        ...                0.500000        0.000000              777   \n",
       "178       ...                0.500000        0.000000              777   \n",
       "138       ...                0.500000        0.000000              777   \n",
       "158       ...                0.500000        0.000000              777   \n",
       "238       ...                0.500000        0.000000              777   \n",
       "198       ...                0.500000        0.000000              777   \n",
       "\n",
       "     split0_train_score  split1_train_score  split2_train_score  \\\n",
       "486            0.916667            0.907643            0.923567   \n",
       "506            0.916667            0.907643            0.923567   \n",
       "526            0.916667            0.907643            0.923567   \n",
       "546            0.916667            0.907643            0.923567   \n",
       "330            0.900641            0.910828            0.917197   \n",
       "408            0.913462            0.907643            0.920382   \n",
       "428            0.913462            0.907643            0.920382   \n",
       "448            0.913462            0.907643            0.920382   \n",
       "390            0.900641            0.910828            0.917197   \n",
       "468            0.913462            0.907643            0.920382   \n",
       "350            0.900641            0.910828            0.917197   \n",
       "370            0.900641            0.910828            0.917197   \n",
       "702            0.913462            0.907643            0.923567   \n",
       "624            0.916667            0.907643            0.923567   \n",
       "642            0.913462            0.907643            0.923567   \n",
       "604            0.916667            0.907643            0.923567   \n",
       "760            0.913462            0.907643            0.923567   \n",
       "740            0.913462            0.907643            0.923567   \n",
       "682            0.913462            0.907643            0.923567   \n",
       "720            0.913462            0.907643            0.923567   \n",
       "780            0.913462            0.907643            0.923567   \n",
       "584            0.916667            0.907643            0.923567   \n",
       "662            0.913462            0.907643            0.923567   \n",
       "564            0.916667            0.907643            0.923567   \n",
       "430            0.919872            0.920382            0.923567   \n",
       "410            0.919872            0.920382            0.923567   \n",
       "450            0.919872            0.920382            0.923567   \n",
       "470            0.919872            0.920382            0.923567   \n",
       "250            0.894231            0.907643            0.914013   \n",
       "190            0.894231            0.907643            0.914013   \n",
       "..                  ...                 ...                 ...   \n",
       "76             1.000000            1.000000            1.000000   \n",
       "716            1.000000            1.000000            1.000000   \n",
       "136            1.000000            1.000000            1.000000   \n",
       "416            1.000000            1.000000            1.000000   \n",
       "638            1.000000            1.000000            1.000000   \n",
       "696            1.000000            1.000000            1.000000   \n",
       "258            1.000000            1.000000            1.000000   \n",
       "398            1.000000            1.000000            1.000000   \n",
       "278            1.000000            1.000000            1.000000   \n",
       "298            1.000000            1.000000            1.000000   \n",
       "358            1.000000            1.000000            1.000000   \n",
       "478            1.000000            1.000000            1.000000   \n",
       "458            1.000000            1.000000            1.000000   \n",
       "78             1.000000            1.000000            1.000000   \n",
       "418            1.000000            1.000000            1.000000   \n",
       "98             1.000000            1.000000            1.000000   \n",
       "218            1.000000            1.000000            1.000000   \n",
       "118            1.000000            1.000000            1.000000   \n",
       "18             1.000000            1.000000            1.000000   \n",
       "438            1.000000            1.000000            1.000000   \n",
       "338            1.000000            1.000000            1.000000   \n",
       "318            1.000000            1.000000            1.000000   \n",
       "378            1.000000            1.000000            1.000000   \n",
       "38             1.000000            1.000000            1.000000   \n",
       "58             1.000000            1.000000            1.000000   \n",
       "178            1.000000            1.000000            1.000000   \n",
       "138            1.000000            1.000000            1.000000   \n",
       "158            1.000000            1.000000            1.000000   \n",
       "238            1.000000            1.000000            1.000000   \n",
       "198            1.000000            1.000000            1.000000   \n",
       "\n",
       "     split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "486            0.910828            0.917197          0.915180         0.005520  \n",
       "506            0.910828            0.917197          0.915180         0.005520  \n",
       "526            0.910828            0.917197          0.915180         0.005520  \n",
       "546            0.910828            0.917197          0.915180         0.005520  \n",
       "330            0.910828            0.914013          0.910701         0.005557  \n",
       "408            0.910828            0.914013          0.913266         0.004214  \n",
       "428            0.910828            0.914013          0.913266         0.004214  \n",
       "448            0.910828            0.914013          0.913266         0.004214  \n",
       "390            0.910828            0.914013          0.910701         0.005557  \n",
       "468            0.910828            0.914013          0.913266         0.004214  \n",
       "350            0.910828            0.914013          0.910701         0.005557  \n",
       "370            0.910828            0.914013          0.910701         0.005557  \n",
       "702            0.910828            0.917197          0.914539         0.005496  \n",
       "624            0.910828            0.917197          0.915180         0.005520  \n",
       "642            0.910828            0.917197          0.914539         0.005496  \n",
       "604            0.910828            0.917197          0.915180         0.005520  \n",
       "760            0.910828            0.917197          0.914539         0.005496  \n",
       "740            0.910828            0.917197          0.914539         0.005496  \n",
       "682            0.910828            0.917197          0.914539         0.005496  \n",
       "720            0.910828            0.917197          0.914539         0.005496  \n",
       "780            0.910828            0.917197          0.914539         0.005496  \n",
       "584            0.910828            0.917197          0.915180         0.005520  \n",
       "662            0.910828            0.917197          0.914539         0.005496  \n",
       "564            0.910828            0.917197          0.915180         0.005520  \n",
       "430            0.914013            0.914013          0.918369         0.003776  \n",
       "410            0.914013            0.914013          0.918369         0.003776  \n",
       "450            0.914013            0.914013          0.918369         0.003776  \n",
       "470            0.914013            0.914013          0.918369         0.003776  \n",
       "250            0.898089            0.910828          0.904961         0.007562  \n",
       "190            0.898089            0.910828          0.904961         0.007562  \n",
       "..                  ...                 ...               ...              ...  \n",
       "76             1.000000            1.000000          1.000000         0.000000  \n",
       "716            1.000000            1.000000          1.000000         0.000000  \n",
       "136            1.000000            1.000000          1.000000         0.000000  \n",
       "416            1.000000            1.000000          1.000000         0.000000  \n",
       "638            1.000000            1.000000          1.000000         0.000000  \n",
       "696            1.000000            1.000000          1.000000         0.000000  \n",
       "258            1.000000            1.000000          1.000000         0.000000  \n",
       "398            1.000000            1.000000          1.000000         0.000000  \n",
       "278            1.000000            1.000000          1.000000         0.000000  \n",
       "298            1.000000            1.000000          1.000000         0.000000  \n",
       "358            1.000000            1.000000          1.000000         0.000000  \n",
       "478            1.000000            1.000000          1.000000         0.000000  \n",
       "458            1.000000            1.000000          1.000000         0.000000  \n",
       "78             1.000000            1.000000          1.000000         0.000000  \n",
       "418            1.000000            1.000000          1.000000         0.000000  \n",
       "98             1.000000            1.000000          1.000000         0.000000  \n",
       "218            1.000000            1.000000          1.000000         0.000000  \n",
       "118            1.000000            1.000000          1.000000         0.000000  \n",
       "18             1.000000            1.000000          1.000000         0.000000  \n",
       "438            1.000000            1.000000          1.000000         0.000000  \n",
       "338            1.000000            1.000000          1.000000         0.000000  \n",
       "318            1.000000            1.000000          1.000000         0.000000  \n",
       "378            1.000000            1.000000          1.000000         0.000000  \n",
       "38             1.000000            1.000000          1.000000         0.000000  \n",
       "58             1.000000            1.000000          1.000000         0.000000  \n",
       "178            1.000000            1.000000          1.000000         0.000000  \n",
       "138            1.000000            1.000000          1.000000         0.000000  \n",
       "158            1.000000            1.000000          1.000000         0.000000  \n",
       "238            1.000000            1.000000          1.000000         0.000000  \n",
       "198            1.000000            1.000000          1.000000         0.000000  \n",
       "\n",
       "[800 rows x 24 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(rbf_grid.cv_results_).sort_values('rank_test_score', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment:** \n",
    "\n",
    "The above table lists the models tested by grid search, ranked by cross-validation test scores.\n",
    "\n",
    "A radial kernel performs best, with a mean CV accuracy of 0.903 which is the same as that achieved by the linear kernel (to 3 d.p.) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. This problem involves the OJ data set which is part of the ISLR package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "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>Purchase</th>\n",
       "      <th>WeekofPurchase</th>\n",
       "      <th>StoreID</th>\n",
       "      <th>PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <th>DiscCH</th>\n",
       "      <th>DiscMM</th>\n",
       "      <th>SpecialCH</th>\n",
       "      <th>SpecialMM</th>\n",
       "      <th>LoyalCH</th>\n",
       "      <th>SalePriceMM</th>\n",
       "      <th>SalePriceCH</th>\n",
       "      <th>PriceDiff</th>\n",
       "      <th>Store7</th>\n",
       "      <th>PctDiscMM</th>\n",
       "      <th>PctDiscCH</th>\n",
       "      <th>ListPriceDiff</th>\n",
       "      <th>STORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CH</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.24</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CH</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>No</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CH</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.09</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.40</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.091398</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MM</td>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CH</td>\n",
       "      <td>228</td>\n",
       "      <td>7</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.956535</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Purchase  WeekofPurchase  StoreID  PriceCH  PriceMM  DiscCH  DiscMM  \\\n",
       "1       CH             237        1     1.75     1.99    0.00     0.0   \n",
       "2       CH             239        1     1.75     1.99    0.00     0.3   \n",
       "3       CH             245        1     1.86     2.09    0.17     0.0   \n",
       "4       MM             227        1     1.69     1.69    0.00     0.0   \n",
       "5       CH             228        7     1.69     1.69    0.00     0.0   \n",
       "\n",
       "   SpecialCH  SpecialMM   LoyalCH  SalePriceMM  SalePriceCH  PriceDiff Store7  \\\n",
       "1          0          0  0.500000         1.99         1.75       0.24     No   \n",
       "2          0          1  0.600000         1.69         1.75      -0.06     No   \n",
       "3          0          0  0.680000         2.09         1.69       0.40     No   \n",
       "4          0          0  0.400000         1.69         1.69       0.00     No   \n",
       "5          0          0  0.956535         1.69         1.69       0.00    Yes   \n",
       "\n",
       "   PctDiscMM  PctDiscCH  ListPriceDiff  STORE  \n",
       "1   0.000000   0.000000           0.24      1  \n",
       "2   0.150754   0.000000           0.24      1  \n",
       "3   0.000000   0.091398           0.23      1  \n",
       "4   0.000000   0.000000           0.00      1  \n",
       "5   0.000000   0.000000           0.00      0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oj_df = pd.read_csv('./data/OJ.csv', index_col=0)\n",
    "oj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'WeekofPurchase+StoreID+PriceCH+PriceMM+DiscCH+DiscMM+SpecialCH+SpecialMM+LoyalCH+SalePriceMM+SalePriceCH+PriceDiff+Store7+PctDiscMM+PctDiscCH+ListPriceDiff+STORE'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(a) Create a training set containing a random sample of 800 observations, and a test set containing the remaining observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = \"+\".join(oj_df.columns.drop(\"Purchase\"))\n",
    "f = f'Purchase ~ {preds}'\n",
    "y, X = pt.dmatrices(f, oj_df)\n",
    "y = y[:, 0]\n",
    "\n",
    "# scale data\n",
    "X = preprocessing.scale(X)\n",
    "\n",
    "# Split training test sets\n",
    "test_size = len(y)-800\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) Fit a support vector classifier to the training data using cost=0.01, with Purchase as the response and the other variables as predictors. Use the summary() function to produce summary statistics, and describe the results obtained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = svm.LinearSVC(C=0.1, random_state=0).fit(X_train, y_train)\n",
    "\n",
    "df = pd.DataFrame({'feature': oj_df.columns.drop('Purchase'),\n",
    "              'coef': np.ravel(model.coef_)[1:]})\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.barplot(x='coef', y='feature', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) What are the training and test error rates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy: 0.840\n",
      "test accuracy : 0.830\n"
     ]
    }
   ],
   "source": [
    "accuracy_train = model.score(X_train, y_train)\n",
    "accuracy_test  = model.score(X_test, y_test)\n",
    "print(f'train accuracy: {accuracy_train:.3f}')\n",
    "print(f'test accuracy : {accuracy_test:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(d) Use the tune() function to select an optimal cost. Consider values in the range 0.01 to 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'C': array([ 0.01   ,  0.02154,  0.04642,  0.1    ,  0.21544,  0.46416,\n",
       "        1.     ,  2.15443,  4.64159, 10.     ])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_range     = np.logspace(-2, 1, 10)\n",
    "param_grid  = dict(C=C_range)\n",
    "model_grid  = GridSearchCV(svm.LinearSVC(), param_grid=param_grid, cv=5, \n",
    "                           scoring='accuracy', return_train_score=True)\n",
    "model_grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(e) Compute the training and test error rates using this new value for cost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy: 0.835\n",
      "test accuracy : 0.841\n"
     ]
    }
   ],
   "source": [
    "accuracy_train = model_grid.score(X_train, y_train)\n",
    "accuracy_test  = model_grid.score(X_test, y_test)\n",
    "print(f'train accuracy: {accuracy_train:.3f}')\n",
    "print(f'test accuracy : {accuracy_test:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(f) Repeat parts (b) through (e) using a support vector machine with a radial kernel. Use the default value for gamma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy: 0.849\n",
      "test accuracy : 0.848\n"
     ]
    }
   ],
   "source": [
    "C_range     = np.logspace(-2, 1, 10)\n",
    "param_grid  = dict(C=C_range)\n",
    "model_grid  = GridSearchCV(svm.SVC(kernel='rbf'), param_grid=param_grid, cv=5, \n",
    "                           scoring='accuracy', return_train_score=True)\n",
    "model_grid.fit(X, y)\n",
    "\n",
    "accuracy_train = model_grid.score(X_train, y_train)\n",
    "accuracy_test  = model_grid.score(X_test, y_test)\n",
    "print(f'train accuracy: {accuracy_train:.3f}')\n",
    "print(f'test accuracy : {accuracy_test:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(g) Repeat parts (b) through (e) using a support vector machine with a polynomial kernel. Set degree=2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy: 0.804\n",
      "test accuracy : 0.767\n"
     ]
    }
   ],
   "source": [
    "C_range     = np.logspace(-2, 1, 10)\n",
    "param_grid  = dict(C=C_range)\n",
    "model_grid  = GridSearchCV(svm.SVC(kernel='poly', degree=2), param_grid=param_grid, cv=5, \n",
    "                           scoring='accuracy', return_train_score=True)\n",
    "model_grid.fit(X, y)\n",
    "\n",
    "accuracy_train = model_grid.score(X_train, y_train)\n",
    "accuracy_test  = model_grid.score(X_test, y_test)\n",
    "print(f'train accuracy: {accuracy_train:.3f}')\n",
    "print(f'test accuracy : {accuracy_test:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(h) Overall, which approach seems to give the best results on this data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support vector machine with radial kernel and cost chosen by grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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