{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "powerful-removal",
   "metadata": {},
   "source": [
    "## Regression with bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "palestinian-headline",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import tree, ensemble, datasets, metrics, model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "weird-permission",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "boston = datasets.load_boston()\n",
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "equipped-hostel",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "y = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "stone-winner",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train samples: 354\n",
      "test samples 152\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, train_size=0.7)\n",
    "\n",
    "print('train samples:', len(X_train))\n",
    "print('test samples', len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "incoming-oxygen",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 419.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train = pd.DataFrame(y_train, columns=['target'])\n",
    "df_train['type'] = 'train'\n",
    "\n",
    "df_test = pd.DataFrame(y_test, columns=['target'])\n",
    "df_test['type'] = 'test'\n",
    "\n",
    "df_set = df_train.append(df_test)\n",
    "\n",
    "_ = sns.displot(df_set, x=\"target\" ,hue=\"type\", kind=\"kde\", log_scale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "historical-consultation",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_estimator = tree.DecisionTreeRegressor(max_depth=4, criterion='mse')\n",
    "model = ensemble.BaggingRegressor(n_estimators=10)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "maritime-injury",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VfAVugRIToxWdPLV0y9hkgd+YUqps0TFVZ/FVy5Ywe7azKGvpUmjfvsxbvQV0Afp2blyiJ17RgmGRKDFsQmdDPcaUUiWHJ9atg4ED4cMPnRr5EydCC9+98lDmECoy5DS0ewuvk9SWbhldYQv8IlID+BQ4wHWdaar6gIg0A94EDgGWAVequhPKjIm+SA5PVDjjpagIxo6Fe+4BEefx4MHMXLmZx7MW+j1vJBZdWbplbApnj38vcIaq7hSRFOBzEfkAuA0Yrapvish4YADwfBjbYUxIIrUatMIZL2vXOkXVFi+GHj2comqNG8dcJk1V38YyHvkd4xeROv7+8/dZdex0PU1x/afAGcA01/EpQGbFbsGYylWZq0H9TRKXu8BYQYGzKUr79vDtt85mKXPmQOPGFTuvSRiBevzLcIK1AI2B7a7H6cAGoJm/D4tIsuscRwJjgZ+APFUtdL1lI+C1KyAiA4GBAI1dv9DGREJlDU8E6nmXa0hp+XLo39/Z77ZXL6defv36QX3eMmmMm9/Ar6rNAERkIvCOqs5xPT+HIHrqqloEtBeRdOAdwPsWPt4/OwGYAM7K3WA/Z0xlqIzhiUCTxCENKeXnw4gR8MQTUK+es/F5ZqbX61rhMhNIsOmcnd1BH0BVPwBODPYiqpoHLAK6AOki4v6D0wiI8Rw5k8gqks8fqOcd9JDSp586tfJHjYKrroJvvvEZ9EM6r0lYwU7ubhKR+4BXXc/7Apv8fUBE6gEFqponIqnAWcAonD8Al+Jk9vQDZpWn4caEW0UnSQP1vL0NKZ1+dD0en/cdt05dwZE1innhm+kc/vYUaNYMPvoIunVzMoFeyPE5DGWZNCaQoIq0uSZyHwBOwRnz/xR4UFX/9POZtjiTt8k43yzeUtUHReRwnKBfB8gBrlDVvf6ub0XaTKR4plcmiVDk5d9HsAXKQi205vn+037K5pF5Y2nw9zZ+/vcAjpzwNNSsWaHibSbxVKhImyvADxGRmqq6K8jPfA108HL8Z+CEYM5hTCSVDqregj4EP0kaas/78XnfccBf23l4wUQuWbOI7w9pzCVXPM6WVh34ombN/e+pcovLTMwJKvCLyInAi8CBQGMRaQcMUtXrw9k4YyIpmJ2rILRJ0tLB351SWSZIq9Lhf/MZ/tF4au3ZyTMnXs7YLr3ZVy0F8vLpmrWQod1bWMaOqRTBjvGPBroD7wKo6koROSVsrTImCoIJnqFOkgY1T7BpE1x/Pc+9O4uvDz2SK3o/zLf/Kpkp7f5celoK23cXlLmOZeyYUARdpE1Vfy11KHDXyJgqxFfwTBYpV4EyCLCYShUmTXKKqs2bxwf9bqPnlU+WCfqen1PFMnZMhQXb4//VNdyjrvILQ4C14WuWMeHhrzaOr4JiFZk49fUtInndL3DmI7BwIZx6Kh/e+jC3Ze+kMMBQ0478Akb3bm8ZO6ZCgg38g4FncFbZ5gLzARvfN1VKoGGXQOmV5QmypVM6k4qLuGrZ+wz97BWokQLjx8O11zL8sY+Dnl+w2jemooIN/C1Uta/nARHpCnxR+U0yJjyCyYjxDKqVUezM81tE863reeyDZ+mw+Ts+bX4Cd515PUnbD2Poys1hmV8wxpdg8/iXq+qxgY6Fi+Xxm8rQdNhsn6+tyzqvzDFfG42DM94fbO9/1tJf2HrvCP6z8DV21UjjoTMHMePoU5wyyjgBvUZKktdJ22QRilVtSMeUS7ny+EWkC05phnoicpvHSwfjLMwypspI9rEgK9kVgEvz1wsPuvf/1VdcdM0AWLUKLr+ci4+4jDWFB5R4S35BEQdUSyI1JdkWZpmICJTVUx0nd78acJDHf3/hlF0wpsrwtSDL1/FAKZL+Sh2/t/hHXj+lN0WdOrN1/Sa+HD0Z3niDb0oFfbcd+QUV2uLQmFAEqs75CfCJiLysqusj1CZjwiLDR+2cDB8B3luWT2nevhV8PvFt2t51C022b+L1dj0YefrVFP5xMCNzcv3W77FJWxMpwebxv+gqrQyAiNQWkXnhaZIx4RFq1UrPjcZ9KfGtYMcOGDyYkwb2QlXpc/mj3NPjRv4+oOb+bwdWOdPEgmCzeuq6SisDoKrbReRf4WmSMeFRnqqV7l64r+Jo+wP27NkwaBBs3szE4y/myZP7sielRolzbcrLt8qZJiYEG/iLRaSxqm4AEJEmOFU6jalSyjuc4jNgN6oOffvC669D69YwYwYvL9zJHtsIxcSwYAP/vTibpX+Cs/Xiybi2RTQmUZTI8V++kZxRz3Pyu2M4eN9ufhx0G8c8OxKqV2doiu9vB7G2EbpJTMGWZZ4rIscCnV2HblHVbeFrljHR4a2kA5Ts6V9Ut4jjH7uPET8sZUWDo7jznJv5td4RjFyz1ecKYPdwTteshVZW2USd3wVcInK0qn7rCvplqOrysLXMgy3gMpFw38xVvPblBp9jmKLFXL5yPncvmkxKcRFPnHIlLx13AcVJzmRtMBu0NBs22+v5Bfil1CIyf3WFyvM+k3jKuxHL7cC1wJNeXlMg8DZExsS4mTm5jHhvjdeVs25Ntm8ia+4YumxYxeLGbRnW4yY21G5Q4j3BlF0IdiN0b0NCt05dwS1TV5RYNWxDR6Y8AuXxX+v6eXpkmmNMZHnL1vGUXFzE1dmzuP2z1yhISuauHjcxte3Z+8steApm8tZXBdDS6Zze6gq5vyl4BnfbkcuUR6CSDT39va6qMyq3OcZEhnt4xFctHoAWW9cx6oNnaL/5Bz488gTuO/t6fj+oLuAMzXgO2XgL3v6GYAINzQT69uAO7rYjlymPQEM9F7h+/gunZs9C1/PTgcWABX5T5QTq5VcvLOCG/73F9V++xY4aB3LjhXfy/tEnlyiqdslxGSz6dqvP4B1sCWhffA0JeXJfO5ihI2M8BRrquRpAROYDLVV1s+t5A+DlsLfOmBD462F7vpbko1gbQPtN3zHqg2dosW0DM1qdzkNnXMP2tFr7X09PTWH4ha0CBu6KDsEEUy7CfY/BDB0Z4ynYPP7D3EHf5XegcRjaY0y5+OthAyVe8xb0U/ft4fbPXqF/9rv8dtAhXH3pA3xyxPHUSktBdheEnC1T0SEYzyGh3Lx8n0NLthLYlEewgX+BqzbPG67nvYGPwtMkY0Lnd29b12NfuqxfSdbcMTTJ+41XOpzLqFOvYleNNEb3al/uAFoZQzClN4XxFdytuJsJVbALuG4UkYuBU1yHJqjqO+FrljGh8TUe7u4te3Pwnp3cvWgyfb6ezy+1G9C7z0iWNG7jvFjBgiSVPQRjwd1UpmB7/ADLgb9V9SMRSRORg1T173A1zBhvfK2sLT0U4iZArdQU8vJL5uif9cOXPDx/HHV35TG+0yWM7vpv9qaUrJV/94xVZK//k0XfbiU3L3//Ri7B7L5lQzAmlgW79eK1OLV56qjqESLSHBivqt3C3UCwlbvG4W1lrb9tC91qp6Wwc28hBUXKIbvyGP7RC1zw7WesrdeUO88ZwqoGzX1+1tcfFAh+oteYaCnvyl23G4ATgCUAqvqDlWU2kTQzJ9drOYX8giK/4/cA23cXkISSueZjHvhoAmkF+Txx8hWM73Qphcn+/wn46xbl5RfYKllTJQUb+Peq6j5x5TGLSDWsLLMJE8/FVb72yQ1Fg7+28si8sZzxczbLG7bgznOG8GPdyklKs1WypioKNvB/IiL3AKkichZwPfBe+Jplwi1WC3uVTsusSNAXLabvirnc9fFLJGsxI7pdy5Rjz99fVK3M+ynZm/E3zOPJVsmaqibYwH8XcA2wChgEzAFeDFejTHjFcmEvb2mZ5dHsz1yyPniWThvX8FmT9tzd40Y2ph/q8/0C9O3cmNlfb94/X5CakkRBkVJQ7D/82ypZU9UEDPwikgysUdWjgYnhb5IJt1gu7BWoTEEgycVFXPPVO9z6+evsTU5h6DlDeLvNmV6Lqnlyh/Y8j0ni3QXFpCQL6a6sIBEo/QXEVsmaqihg4FfVIhH5znPrxWCIyGHAf4H6OP+uJqjqMyJSB5gKNAXWAb1UdXt5Gm/KJ9yFvSoyjFSRMf1jtvzMY3Oeoc3vPzH3qC7cf9Z1bD2wTlCfTU9N8Tp5XFCk5OUX7E/hBEvRNFVfsEM9tYE1IrIU2OU+qKoX+vlMIXC7qi4XkYOAZSLyIXAVsEBVs0RkGDAMZyjJREg4C3tVdBipPEG/emEBNy5+k+uWTCOvxkFcd9EwPmjRNWAv301w3urvyu77GNmzTcDNVoyJdcEG/vtDPbGrts9m1+O/RWQtkAFcBJzmetsU4GMs8EdUOAt7hTqMVPrbQbqXxVb+HJu7llEfPEvzP35leuszeOiMa8hLPTikNvft3JjXvgz8ZTZWhsOMqahA9fhrAIOBI3EmdiepamGoFxGRpkAHnHUA9T0Kvv2GMxTk7TMDcW3o3rix1YOrTOFaVTozJ9fnGL23YSRv3w6Sk4Lrpafty+eOT1/hqmXvsenguvS7bASfHH4ctdNSePqCViXu7fSj6zF9WW6ZP0hpKUk82rMtmR0yeGd5Lrv2BZ5UtgweEw8C9finAAXAZ8A5QEtgSCgXEJEDgek4G7T/JR5fv1VVRcTrN2xVnQBMAGflbijXNIFVdu0XdxD3xdswkrdvB0UBMmgATvolh5HznuOwHb8z5djzeOyUfuw6II3UlGQeuKCV13vr2KSO3z90u4MI+r7uw5iqJlDgb6mqbQBEZBKwNJSTi0gKTtB/zWO3rt9FpIGqbnbV9d8SaqNN7PGXhukeRvIc1klPS/FbZsGbg/fs5L6FL9Jr1Uf8VCeDy/6dxVeHtQb8l08IZrI5mJ6FZfCYeBEo8O//l6mqhRLkZBmAOG+eBKxV1ac8XnoX6AdkuX7OCvqkJmb5GwIZ2dOpeOk5rBNq0O/+/WIemv88dXbvYGzny3i2ax/2Vqu+//UVD5zt9XPBTjb7yyYSsAweE1cCBf52IvKX67HgrNz9y/VYVdXfLFpX4EpglYiscB27ByfgvyUiA4D1QK/yNt7EDl+ZQhnpqWR2yKBr1sKgFmalJDvpNe5FU/V2bmf4R+M577svWPOvw7n60gdYc+iRQbfL12Tz7W+tBP4J/n06HcarXiZ4r+jcmIcz2wR9PWOqgkBbL3pf2x4EVf0cfJZCj0hVTxM5gTKFgp0UffzSdgAMn7Wabl/N4/6FE0kt2Mtjp/yHCSf09FpULcPPuLuvyeYi1RI9f3dwf2PJrxSpkixCn06HWdA3cSmUevwmgYS6CCtQplAwm4e7vx2wfj2ZX46GefP4ukkrbj3rRv487HCKfQwPnX50PZ/34K/eTun0zIcz21igNwnBAr8po7yLsPxlCgXaPDw1JZmhZzWH556DYcOcg2PG0Pb661mQlOTRrq/JLygu8dnpy3Lp2KROmWs/Pu+7gJO2lp5pElFStBtgYk+g/WtDMTMnl65ZC7l16goOqJZE7bQUBGdzlPRU53FGeipjjk0jc0gfuOkmOOkkWLMGbrwRkv75Fc3skEGdmgeUuYavtgUT1C090yQi6/GbMnwFzNy8fLpmLQx6+Kf0N4e8/AJSU5IZ3dtjE/OCAnjiCegzAtLSWDZiNDentGHT86tpmP5TmWuEUmco0PCSpWeaRGU9flOGr16w4AR/5Z/hn5k5uT7PE/CbQ04OdOoE99wDF1zAB1MXcEXB0eTu2OPzGr7a5u340O4tSE0pmZ/gzjbISE9lZM82lp5pEpIFflOGr4DpbdtDf8M/vnrn27btcIL98cezZ8NG7uk7nGZHXMWNCzcHHGLy1jZfPffMDhmM7NmGjPTU/UNKo3u3Z13WeXwx7AwL+iZh2VCPKcNbhk6oNXh8Tax23LiGp+aNgW0bWXZGJv3bXM6OGgcCvitzel4j1DpDlV2awph4IFrB/UwjoWPHjpqdnR3tZiS0rlkLvQb/2mkppFWvFrAgWs29u7nz0yn0Wz6b3Q0akXPfKK7YUCuoUgkZ6alWCtmYchCRZarasfRx6/Ebnzxz+WulppCSLBQU/ROqU5KFnXsK95dfyM3L97qZySk/L+PRec/R8O9t/NSnP0dMeIY7n1uKEjjrxiZgjal8FviNV94yclKShNppKeTtLqBheiq79haWqZ3vGfRr5f/N/y2cyCWrF/JjnUYkff45R5x4IuA/1TJZhGLVEsM4sbo5vDFVkQX+OFfegDnivTVlhmsKipW06tXI+T+nIFqzYbN9fv6cbz/nwQ/Hk77nb8Z06c2Mc65ikSvoA9Tys+HKk73alWhjLG8Ob0xVZIE/jpU3YM7MyfVZPdOzp+5t0rfezj956MPn6fH9/1hV/wj69XqQXxo1Z+T5JUsh+Cr0mpaS5HUFbqxuDm9MVWTpnHGsvCtwR7y3xudrnvnyJVIrVbns6w/5aNL1dPtlGeN6XMPF/3mKHS1aec2Xz/Pxh6V0OQYI/+bwxiQa6/HHsfIGTH+18j0LormD+atvfMwtbz3BSetXsK1DJ1Le/C/XH3UU1/u5Rigbvodzc3hjEpH1+KsAd72bZsNm0zVrod/Vsp5CWeUarKlf/frP9YuKyPxsOtPGDuSkP36EceOom70Yjjoq4HlCWYgVynuNMYFZ4I9x7nH6UEoluJU3YKanpvh8raBInaGgtWv549hOMGQIiw49hpOuHEPT9Y3p+tjHQbXN26paXyUUQnmvMSYwW8AV43wtnAp2UVN5snpm5uQy9O2V+3fB8lStqJDBS6Zx65dv8Xe1AxjebSAzW55WYrY2NSXZArMxMcDXAi4L/DGu2bDZXle3CvBL1nlhu+7MnFxumbqixLHWv/3I43Oe5pit61jQ5jTuPPUa/qiZ7vXz3nLxjTGR5Svw21BPjAvHOH0wMjtk7B/yOaBgL8M+folZ/72NOvl/MfDiexlw7h0+gz44dXdCHZoyxkSGBf4YF82JzeEXtuLEjWv44KWbGLxkOm+1OZOzBoxj/lFdfG6m7E15N3ExxoSHpXPGuFCrUVaav/4ic+IjZL72PBtq1effvR9mcdP2+19WvJdq9sVy7o2JHRb4q4CIlxaeMwcGD4aNG+HWW+lBV3ZXr1HmbYozyZybl0+yCEWq+3+WZjn3xsQOC/zmH9u2wa23wquvQsuWsHgxdO5M7ayF7A4ys6h0mQiwnHtjYo2N8RtQhalTnWD/5pvwf/8Hy5dD585AxXe9stROY2KLpXMmuk2b4Lrr4N132d6yHUPOvIHPUhuWmUuwssjGVD22EYspSRUmTYI77oC9e1l9y31cntaZna4RmtKVPG0LQ2Pihw31JKKff4Yzz4Rrr4X27WHVKgbVP31/0HezNExj4pP1+ONA0MMwRUXw7LNw771QrRq88AJccw0kJbEpz3uAz83Lp2vWQhviMSaOWOCv4oLebGX1ahgwAJYuhfPOg/HjoVGj/S/7Kn3sPqffcxtjqhQb6okh5Sm/HHCzlX37YMQIOPZY9n7/Iw/0vpdmrQbT9dXv959/Zk4uu/cVBtVGG/4xpuoLW1aPiEwGzge2qGpr17E6wFSgKbAO6KWq2wOdKxGyenzlvwdKhfRbxO2Sf0H//rB6Nb/2uIjex1zOpuoHlXhPqCtw3TJs2MeYmBeNIm0vAz1KHRsGLFDV5sAC13ND+bdJ9LYitkbBHu5eOImiTp3J37IN3n2Xy0+9uUTQh3+CfXn+9FvxNWOqrrAFflX9FPiz1OGLgCmux1OAzHBdv6op7zaJpRdXdd7wNXMn38TAr97hzXZnc+IVzzKz0bFhqZVjwz7GVE2Rntytr6qbXY9/A+r7eqOIDAQGAjRu3DgCTQuPYDNuyruvrPtc42Yt46qZz/PvlXNZl96APpc/yv+atAWczdP9Td56E+zwjxVfM6bqidrkrjqTCz5ji6pOUNWOqtqxXr16vt4W00LZNrEi5ZczNy5n/sTr6P31fF44oSc9+o/ZH/TB2Tzd2/l9SU1Jpm/nxiXKLtRO874doxVfM6bqiXSP/3cRaaCqm0WkAbAlwtePKH/j9qV7/eUqv7x1KwwZAm+8Aa1b07Pb7axs6P0Phef5c/Pyy/To3c99Tdpa8TVj4kekA/+7QD8gy/VzVoSvH1GhjtuXDv7u8fMywV/VCfY33wx//eWkaw4bxvqRH0N+QZnzunfS8iy7EGrtnajtC2CMqXRhC/wi8gZwGlBXRDYCD+AE/LdEZACwHugVruvHglDH7YNajLVxo1NU7f33oVMnp95Oq1aAs2NW6U3SU5KE4Re2KnOt8tTesXo9xsSHcGb19FHVBqqaoqqNVHWSqv6hqt1UtbmqnqmqpbN+4kqo4/Z+UzqLi+GFFyg4+hj2zPuQh864hpMvfIiZ+9L3vzezQwaPX9auxNj845e1s2BtjCnBSjaEUajDI76GgKr//CPbTriXusu+ZGmTtgzrcTO/ph8Kf+0r843AeuXGmEAs8IdZKIG49NBQcnER/b+axe2fv0pBcjXu6nETU9ueDfLPVue+JouNMcYXC/wxZGj3FvvH+I/e8gujPniWdr/9wMctOnNXt8H8flBdr5+zXHpjTCgs8MeQzA4ZJO3by7Z7hnPlx6+zM/UglmY9z9V/NkI9evmlWS69MSYUVp0zlnz5JRf2v4D+C18h5d99qL3+R064azANa6f5/Ijl0htjQmWBPxbs2gW33QYnnujk5c+eDa+8AoccAnjPDgInP982MjfGhMqGeqJtwQJnC8RffnHy87Oy4OCDS7zFFk8ZYyqTBf5oyctzNjqfNAmaN4dPPoFTTvH5dkvTNMZUFhvqiYZZs6BlS3jpJbjzTli50m/QN8aYymSBP5J+/x1694bMTKhXD5YsgVGjINWycowxkWOBPxJU4dVXnV7+zJnw8MOQnQ0dy+yIZowxYWdj/OG2YQMMHgwffABdujhj+sccE+1WGWMSmPX4w6W4GMaNcypnfvIJPPMMfPaZBX1jTNRZjz8cvv8errnGCfRnngkTJkCzZtFulTHGANbjr1yFhc5kbdu2sGoVTJ4M8+db0DfGxBTr8VeWlSuhf39YvhwuvhjGjoUGDaLdKmOMKcN6/BW1Zw/cd5+ToZObC9OmwYwZFvSNMTHLevwVsXgxDBgA334L/frBU09BnTrRbpUxxvhlPf7y2LnT2ej8pJNg926YOxdeftmCvjGmSrDAH6r586F1axgzBm64AVavhu7do90qY4wJmgX+YG3fDldf7QT5GjWcVM0xY+Cgg6LdMmOMCYkF/mDMmOGUW3jlFbj7blixwhnmMcaYKsgmd/357Te48UaYPh3at4c5c6BDh2i3yhhjKsR6/N6oOpO1LVvC++/Do4/C0qUW9I0xccF6/KWtWweDBjmTuF27wosvwtFHR7tVxhhTaazH71Zc7EzWtm4NX3zhPP70Uwv6xpi4Yz1+cBZgXXONE/C7d4cXXoAmTaLdKmOMCYvE7vEXFDjj9+3awTffwJQpTt18C/rGmDiWuD3+5cudcgsrVsCll8Jzz0H9+tFulTHGhF3i9fjz851c/BNOcNI1p0+Ht9+2oG+MSRiJ1eP//HOnl//9984q3CefhNq1o90qY4yJqKj0+EWkh4h8JyI/isiwsF/w77+dhVgnnwz79jmpmpMnW9A3xiSkiAd+EUkGxgLnAC2BPiLSMmwXnDvXSdEcNw6GDHF2xjrrrLBdzhhjYl00evwnAD+q6s+qug94E7goLFcaNAjOOQdq1nRSNZ9+Gg48MCyXMsaYqiIagT8D+NXj+UbXsRJEZKCIZItI9tatW8t3pSOPdHbHysmBLl3Kdw5jjIkzMTu5q6oTgAkAHTt21HKdZOjQymySMcbEhWj0+HOBwzyeN3IdM8YYEwHRCPxfAc1FpJmIVAcuB96NQjuMMSYhRXyoR1ULReRGYB6QDExW1TWRbocxxiSqqIzxq+ocYE40rm2MMYku8Uo2GGNMgrPAb4wxCcYCvzHGJBgL/MYYk2BEtXxroyJJRLYC68v58brAtkpsTlVg95wY7J7jX0Xvt4mq1it9sEoE/ooQkWxV7RjtdkSS3XNisHuOf+G6XxvqMcaYBGOB3xhjEkwiBP4J0W5AFNg9Jwa75/gXlvuN+zF+Y4wxJSVCj98YY4wHC/zGGJNg4jrwR3xT9ygQkckiskVEVnscqyMiH4rID66fcbOrvIgcJiKLROQbEVkjIkNcx+P5nmuIyFIRWem65xGu481EZInr93uqq8x5XBGRZBHJEZH3Xc/j+p5FZJ2IrBKRFSKS7TpW6b/bcRv4I76pe/S8DPQodWwYsEBVmwMLXM/jRSFwu6q2BDoDN7j+f43ne94LnKGq7YD2QA8R6QyMAkar6pHAdmBA9JoYNkOAtR7PE+GeT1fV9h75+5X+ux23gZ9IbuoeRar6KfBnqcMXAVNcj6cAmZFsUzip6mZVXe56/DdOUMggvu9ZVXWn62mK6z8FzgCmuY7H1T0DiEgj4DzgRddzIc7v2YdK/92O58Af1Kbucaq+qm52Pf4NqB/NxoSLiDQFOgBLiPN7dg15rAC2AB8CPwF5qlroeks8/n4/DdwJFLueH0L837MC80VkmYgMdB2r9N/tmN1s3VQOVVURibucXRE5EJgO3KKqfzmdQUc83rOqFgHtRSQdeAc4OrotCi8ROR/YoqrLROS0KDcnkk5S1VwR+RfwoYh86/liZf1ux3OPP5E3df9dRBoAuH5uiXJ7KpWIpOAE/ddUdYbrcFzfs5uq5gGLgC5Auoi4O2/x9vvdFbhQRNbhDNOeATxDfN8zqprr+rkF5w/8CYThdzueA38ib+r+LtDP9bgfMCuKbalUrnHeScBaVX3K46V4vud6rp4+IpIKnIUzt7EIuNT1tri6Z1W9W1UbqWpTnH+7C1W1L3F8zyJSU0QOcj8GzgZWE4bf7bheuSsi5+KME7o3dX8kui2qfCLyBnAaTvnW34EHgJnAW0BjnHLWvVS19ARwlSQiJwGfAav4Z+z3Hpxx/ni957Y4k3rJOJ21t1T1QRE5HKc3XAfIAa5Q1b3Ra2l4uIZ67lDV8+P5nl339o7raTXgdVV9REQOoZJ/t+M68BtjjCkrnod6jDHGeGGB3xhjEowFfmOMSTAW+I0xJsFY4DfGmARjgd/EFRE5xFXZcIWI/CYiuR7PK1zJUUQeEJGRpY61F5G1fj4zXETuqOi1jaksVrLBxBVV/QOngiUiMhzYqapPuF8XkWoetV7K4w1gLnC3x7HLXceNqRKsx2/inoi8LCLjRWQJ8FjpHriIrHYVfENErnDVvl8hIi+4ynvvp6rfA9tFpJPH4V7AGyJyrYh85aqbP11E0ry05WMR6eh6XNdVksBdhO1x1+e/FpFBruMNRORTV3tWi8jJlfu/jklEFvhNomgEnKiqt/l6g4gcA/QGuqpqe6AI6OvlrW/g9PJx1cX/U1V/AGao6vGuuvlrCa1W/ABgh6oeDxwPXCsizYB/A/Nc7WkHrAjhnMZ4ZUM9JlG87apw6U834DjgK1e1z1S8F8SaCiwWkdspOczTWkQeBtKBA4F5IbTvbKCtiLjr0NQCmuPUnJrsKkw3U1VXhHBOY7yywG8SxS6Px4WU/LZbw/VTgCmq6jl+X4aq/ioivwCnApfgVMoEZze0TFVdKSJX4dRQKs3z2jU8jgtwk6qW+WMhIqfgbEjysog8par/9dc+YwKxoR6TiNYBxwKIyLFAM9fxBcClrlro7r1Om/g4xxvAaOBnVd3oOnYQsNnVO/c2ROS+9nGux5d6HJ8HXOf6LCJylKtaYxPgd1WdiLMT1bGh3Kgx3ljgN4loOlBHRNYANwLfA6jqN8B9ODsgfY2z01UDH+d4G2hFyWye+3GqhH4BfOvtQ8ATOAE+B6eiqtuLwDfAchFZDbyA8438NGCl6/29cWrSG1MhVp3TGGMSjPX4jTEmwVjgN8aYBGOB3xhjEowFfmOMSTAW+I0xJsFY4DfGmARjgd8YYxLM/wPzqLY7BkkGoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = model.predict(X_test)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_test, predicted)\n",
    "\n",
    "ax.set_xlabel('True Values')\n",
    "ax.set_ylabel('Predicted')\n",
    "_ = ax.plot([0, y.max()], [0, y.max()], ls='-', color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "refined-service",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residual = y_test - predicted\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_test, residual)\n",
    "ax.set_xlabel('y')\n",
    "ax.set_ylabel('residual')\n",
    "\n",
    "_ = plt.axhline(0, color='red', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "affected-ballet",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.displot(residual, kind=\"kde\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "moral-thursday",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score: 0.8469546442032072\n",
      "mse: 14.279788815789473\n",
      "rmse: 3.7788607828007468\n",
      "mae: 2.411907894736842\n"
     ]
    }
   ],
   "source": [
    "print(\"r2 score: {}\".format(metrics.r2_score(y_test, predicted)))\n",
    "print(\"mse: {}\".format(metrics.mean_squared_error(y_test, predicted)))\n",
    "print(\"rmse: {}\".format(np.sqrt(metrics.mean_squared_error(y_test, predicted))))\n",
    "print(\"mae: {}\".format(metrics.mean_absolute_error(y_test, predicted)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}