Notebooks/03_RandomForest_DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "pending-round",
   "metadata": {},
   "source": [
    "## [Decision Trees](http://www.fairlynerdy.com/Files/Cheat_Sheets/Decision_Trees_Cheat_Sheet.pdf )\n",
    "\n",
    "Decision trees are Powerful but prone to overfitting. It uses an intuitive learning approach akin to how humans make a decision. \n",
    "\n",
    "\n",
    "\n",
    "## Random Forest\n",
    "\n",
    "- Random Forest is based on Decision Trees -> many trees = a forest!\n",
    "- Grows multiple trees on random subsets of the parent dataset\n",
    "- At every split, a new random subset of features is chosen\n",
    "    - `Heuristic is applied to know which is the best feature.`\n",
    "    - This leads to \"decorrelated\" trees, which leads to a large increase in performance!\n",
    "    \n",
    "Read this for a quick overview of [Random Forest](https://medium.com/analytics-vidhya/a-primer-for-random-forests-1019c1d02bcb)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "practical-oracle",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "viral-purpose",
   "metadata": {},
   "source": [
    "### Let us start with loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "suspended-angola",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "federal-specialist",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = datasets.load_diabetes()\n",
    "diabetes_pd = pd.DataFrame(diabetes.data)\n",
    "feature_names = [\"age\", \"sex\", \"bmi\", \"map\", \"tc\", \"ldl\", \"hdl\", \"tch\", \"ltg\", \"glu\"]\n",
    "diabetes_pd.columns = feature_names\n",
    "diabetes_pd[\"y\"] = diabetes.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "personal-edmonton",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "greek-airplane",
   "metadata": {},
   "source": [
    "### Build a Decicion Tree Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alive-deadline",
   "metadata": {},
   "source": [
    "Many of the sklearn algorithms are implemented using the same standard steps: \n",
    "- **Step 1: Initiate the algorithm** Define the parameters (& hyperparameters of the algorithm) of the algorithm. For example, the maximum depth, the minimum samples in a leaf etc. (check documentation for more information)\n",
    "\n",
    "- **Step 2: Train the algorithm** Train the algorithm by fitting it to the X_train and y_train datasets.\n",
    "\n",
    "- **Step 3: Evaluating the algorithm** Evaluate the predictive power of the algorithm by comparing the predictive loan amount values to the true values. We can do this for the training and testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "progressive-mission",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decision_tree_c(X_train, y_train, X_test, y_test):\n",
    "    # Step 1: Initiating the DecisionTreeRegressor algorithm\n",
    "    decision_regressor = DecisionTreeRegressor(random_state=42)\n",
    "    # Step 2: Training the algorithm using the X_train dataset of features and y_train, the associated target features\n",
    "    decision_regressor.fit(X_train, y_train)\n",
    "    # step 3: Print prediction scores\n",
    "    print_scores(decision_regressor, X_train, y_train, X_test, y_test, oob_score=False)\n",
    "    plot_test(decision_regressor, X_train, y_train, 'train')\n",
    "    plot_test(decision_regressor, X_test, y_test, 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "accessible-default",
   "metadata": {},
   "outputs": [
    {
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       "      <th>bmi</th>\n",
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       "      <td>-0.092204</td>\n",
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       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>135.0</td>\n",
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       "      <th>437</th>\n",
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       "      <td>0.050680</td>\n",
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       "      <td>0.059744</td>\n",
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       "      <td>0.007207</td>\n",
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       "      <td>0.034309</td>\n",
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       "      <td>0.044485</td>\n",
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       "          age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2    0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
       "\n",
       "          tch       ltg       glu      y  \n",
       "0   -0.002592  0.019908 -0.017646  151.0  \n",
       "1   -0.039493 -0.068330 -0.092204   75.0  \n",
       "2   -0.002592  0.002864 -0.025930  141.0  \n",
       "3    0.034309  0.022692 -0.009362  206.0  \n",
       "4   -0.002592 -0.031991 -0.046641  135.0  \n",
       "..        ...       ...       ...    ...  \n",
       "437 -0.002592  0.031193  0.007207  178.0  \n",
       "438  0.034309 -0.018118  0.044485  104.0  \n",
       "439 -0.011080 -0.046879  0.015491  132.0  \n",
       "440  0.026560  0.044528 -0.025930  220.0  \n",
       "441 -0.039493 -0.004220  0.003064   57.0  \n",
       "\n",
       "[442 rows x 11 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_pd['y'] = diabetes.target\n",
    "training_df = diabetes_pd.copy()\n",
    "training_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "hired-customer",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_train_data(train_df, pred_var):\n",
    "    \"\"\"\n",
    "    Given a dataframe and the predictive variable, this\n",
    "    function returns train and test arrays generated using  the  \n",
    "    scikit--learn split function\n",
    "    \"\"\"\n",
    "    rf_trainRes = train_df[pred_var]\n",
    "    train_df = train_df.drop(pred_var, axis=1)\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(train_df, rf_trainRes, test_size=0.2, random_state=42)\n",
    "    \n",
    "    train_df[pred_var] = rf_trainRes\n",
    "\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "def train_linear(X, y):\n",
    "    linear_reg_all = linear_model.LinearRegression()\n",
    "    linear_reg_all.fit(X_train, y_train)\n",
    "    print(linear_reg_all.score(X_test, y_test))\n",
    "    return linear_reg_all\n",
    "\n",
    "def print_scores(regressor, X_train, y_train, X_test, y_test, oob_score=True):\n",
    "    print(\"Train score: \" + str(regressor.score(X_train, y_train)))\n",
    "    print(\"Test score: \" + str(regressor.score(X_test, y_test)))\n",
    "    if oob_score:\n",
    "        print(\"OOB score: \" + str(regressor.oob_score_))\n",
    "        \n",
    "def train_linear_r(X_train, y_train, X_test, y_test):\n",
    "    # Create linear regression object\n",
    "    regr = linear_model.LinearRegression()\n",
    "    \n",
    "    # Train the model using the training sets\n",
    "    model1 = regr.fit(X_train, y_train)\n",
    "    \n",
    "    print_scores(model1, X_train, y_train, X_test, y_test, oob_score=False)\n",
    "    plot_test(model1, X_train, y_train, 'train')\n",
    "    plot_test(model1, X_test, y_test, 'test')\n",
    "    \n",
    "def plot_test(regressor, X_test, y_test, data):\n",
    "    rf_y_predicted_test = regressor.predict(X_test)\n",
    "    print(\"Mean squared error: %d \" % round(mean_squared_error(y_test, rf_y_predicted_test)))\n",
    "    fig = plt.figure(figsize=(7, 7))\n",
    "    plt.scatter(y_test, rf_y_predicted_test, alpha=0.5)\n",
    "    plt.xlabel('True value')\n",
    "    plt.ylabel('Predicted value')\n",
    "    plt.title(\"Relationship between True Y and predicted Y* (%s dataset)\" % data)\n",
    "    plt.show()\n",
    "    \n",
    "def prepare_train_data(train_df, pred_var):\n",
    "    \"\"\"\n",
    "    Given a dataframe and the predictive variable, this\n",
    "    function returns train and test arrays generated using  the  \n",
    "    scikit--learn split function\n",
    "    \"\"\"\n",
    "    rf_trainRes = train_df[pred_var]\n",
    "    train_df = train_df.drop(pred_var, axis=1)\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(train_df, rf_trainRes, test_size=0.2, random_state=42)\n",
    "    \n",
    "    train_df[pred_var] = rf_trainRes\n",
    "\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "practical-tradition",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "approved-purpose",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n",
      "Train score: 1.0\n",
      "Test score: 0.058738960879898494\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 4987 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glu\n",
      "Train score: 1.0\n",
      "Test score: -0.14247812430152895\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 6053 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ltg\n",
      "Train score: 1.0\n",
      "Test score: -0.1137337353032144\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 5901 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tch\n",
      "Train score: 1.0\n",
      "Test score: -0.10596337664231559\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 5860 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hdl\n",
      "Train score: 1.0\n",
      "Test score: 0.11009882606812171\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 4715 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ldl\n",
      "Train score: 1.0\n",
      "Test score: -0.010793448894908408\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 5355 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tc\n",
      "Train score: 1.0\n",
      "Test score: 0.17657102090087706\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 4363 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map\n",
      "Train score: 1.0\n",
      "Test score: 0.25410070756166914\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 3952 \n"
     ]
    },
    {
     "data": {
      "image/png": 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5M+c8UNATEcmQPHfmnAcKeiIiGZLnzpzzQEFPRCRD8tyZcx4o6ImIZEieO3POAwU9EZEMyXNnznmQWJMFM+sGNgLTw3a+6u7vN7M5wJeBJcBDwFvc/cnwnquAi4FDwKXu/r2k0iciklV57cw5D5LM6R0AXuPuLwROAVaZ2UuBK4Gb3P1E4KbwGjNbAVwInASsAj5uZlMTTJ+IiBRMYkHPI/vDy67w58C5wLowfx1wXpg+F7je3Q+4+1bgQeD0pNInIiLFk+gzPTObamZ3AruADe7+U2C+uw8ChP/HhtUXANtib98e5pV/5mozGzCzgd27dyeZfBER6TCJBj13P+TupwALgdPNbGWN1a3SR1T4zLXu3u/u/fPmzWtRSkVEpAjaUnvT3fcANxM9qxsysz6A8H9XWG07sCj2toXAznakT0REiiGxoGdm88xsdpjuAc4Efg7cCFwUVrsI+FaYvhG40Mymm9lS4ETgtqTSJyIixZPkKAt9wLpQA3MKcIO7f9vMbgVuMLOLgUeA8wHc/V4zuwG4DzgIXOLuhxJMn4iIFIy5P+OxWW709/f7wMBA2skQEZEMMbPb3b2/0jL1yCIiIoWhQWRFckYDjIpMnHJ6IjmiAUZFJkdBTyRHNMCoyOQo6InkiAYYFZkcBT2RHNEAoyKTo4osIi3Sjgomq1bOZ+3GrUCUw9s3epDhkTEuOG1hS7cj0qmU0xNpgXZVMNEAoyKTo5yeSAvEK5gAh/+v3zTU8oCkAUZFJk45PZEWUAUTkXxQ0BNpAVUwEckHBT2RFli1cj7DI2MMj4zxtPvh6VUr56edNBGJ0TM9kRYoVTCJ19684LSFTT97UxdjIslS0BNpkclWMCnVAO3t6RpXA1S1M0VaR8WbIhmhLsZEkqegJ5IRqgEqkjwFPZGMUA1QkeQp6IlkhGqAiiRPQU8kI9TFmEjyVHtTJEPUxZhIspTTExGRwlDQExGRwlDQExGRwlDQExGRwlDQExGRwlDQExGRwlDQExGRwlDQExGRwlDQExGRwlDQExGRwlA3ZCLS8TQivZQopyciHa00Iv3wyNi4Eek3Dw6nnTRJgYKeiHQ0jUgvcQp6ItLRNCK9xCnoiUhH04j0EqegJyIdTSPSS5yCnoh0NI1IL3FqsiAiHa+TR6RXc4zmKKcnIpJTao7RPAU9EZGcUnOM5inoiYjklJpjNE/P9EREMqSZZ3QLZvcwPDJGb0/X4XlqjlGbcnoiIhnR7DM6NcdonoKeiEhGNPuMTs0xmqfiTRGRjNixZ4S+3u5x8+o9o+vk5hhJUE5PRCQj1GVa8hT0REQyQs/okqegJyKSEXpGlzw90xMRyRA9o0uWcnoiIlIYCnoiIlIYCnoiIlIYiQU9M1tkZj8ys81mdq+ZvTvMv8bMdpjZneHvnNh7rjKzB83sfjM7O6m0iYhIMSVZkeUg8B53v8PMZgK3m9mGsGyNu380vrKZrQAuBE4CjgN+YGbL3P1QgmkUEZECSSzoufsgMBim95nZZmBBjbecC1zv7geArWb2IHA6cGtSaZTKNCildDqd48XVlmd6ZrYEeBHw0zDrXWZ2t5l91syOCfMWANtib9tOhSBpZqvNbMDMBnbv3p1ksgtJg1JKp9M5XmyJBz0zmwF8DbjM3fcCnwBOAE4hygn+bWnVCm/3Z8xwX+vu/e7eP2/evGQSXWAalFI6nc7xYks06JlZF1HA+6K7fx3A3Yfc/ZC7Pw18iqgIE6Kc3aLY2xcCO5NMnzyTBqWUTqdzvNiSrL1pwGeAze7+d7H5fbHV3gRsCtM3Ahea2XQzWwqcCNyWVPqkMnV4K51O53ixJZnTeznwNuA1Zc0T/sbM7jGzu4FXA5cDuPu9wA3AfcB64BLV3Gw/dXgrnU7neLGZ+zMem+VGf3+/DwwMpJ2MjqOabdLpdI53NjO73d37Ky1Th9PyDOrwVjqdzvHiUjdkIiJSGMrpiWRctaI4FdFJs3TOKKcnkmnVGlJ/5+4damAtTVGj/IiCnkiGVWtIve7WR9TAWpqiRvkRBT2RDKvWkHpo76gaWEtT1Cg/oqAnkmHVGlLPn9WtBtbSFDXKjyjoiWRYtYbUF71ssRpYS1PUKD+ioCeSYcv7ell9xlJ6e7oYHB6lt6eL1Wcs5Q0nL6g4v2g18aRx1c6lop0z6pFFREQ6Sq0eWZTTExGRwlDjdBGROtSou3MopyciUoMadXcWBT0RkRrUqLuzKOiJiNSgRt2dRUFPRKQGNeruLAp6IiI1qFF3Z1HQExGpQY26O4uaLIiI1KGR1juHcnoiIlIYCnoiIlIYCnoiIlIYCnoiIlIYCnoiIlIYCnoiIlIYCnoiIlIYaqcnIrmiYX5kMhT0RAouT0GkNMxPb0/XuGF+1ENKevJ0/oCKN0UKLW9jxWmYn2zJ2/kDCnoihZa3IKJhfrIlb+cPKOiJFFregoiG+cmWvJ0/oKAnUmh5CyIa5idb8nb+gIKeSKHlLYhomJ9sydv5A2DunnYaJqy/v98HBgbSToZIruWt9p1kSyvOn1afg2Z2u7v3V1ymoCciImmJN0OZ2T2NfaMHGR4Zm1QOvlbQU/GmiIikpt01QBX0REQkNe2uAaqgJyIiqWl3DVAFPRERSU27a4Aq6ImISGra3QxFHU6LiEiqlvf1tq2ZjHJ6IiJSGAp6IiJSGAp6IiJSGHqmJ3WpmyqR1tPvKh3K6UlNeRwkUiTr9LtKj4Ke1JTHQSJFsk6/q/Qo6ElNeRwkUiTr9LtKj4Ke1JTHQSJFsk6/q/TUDXpmtszMbjKzTeH1yWZ2dfJJkyzI4yCRnWzz4DBrNmzhiq/cxZoNW/QMKKf0u0pP3fH0zOwW4L3AJ939RWHeJndf2Yb01VSU8fTSruWV9vYlksS4Y5Ie/a6SU2s8vUaaLDzL3W8zs/i8g9VWjm10EfA54DnA08Bad/+Ymc0BvgwsAR4C3uLuT4b3XAVcDBwCLnX37zWQvo4Wv9DFa3m180LXzi6CsiCrF6N45Qfg8P/1m4YykT4Zr955VLTfVVY08kzvMTM7AXAAM3szMNjA+w4C73H35cBLgUvMbAVwJXCTu58I3BReE5ZdCJwErAI+bmZTm9yfjqNaXu2VVFXyVhRLqvJDfqhJQnY1EvQuAT4JvMDMdgCXAX9c703uPujud4TpfcBmYAFwLrAurLYOOC9Mnwtc7+4H3H0r8CBwesN70qF0oWuvJG4yWnUBVOWH/NDNanbVLd509/8CzjSzo4EpIYA1xcyWAC8CfgrMd/fB8NmDZnZsWG0B8JPY27aHeeWftRpYDbB48eJmk5I7C2b3MDwydrgoC3ShS9KOPSP09XaPmzfZm4xWFUuuWjmftRu3Hk5T6ZneBactnHDaiqgdxddJnEfSGnWDnpn9RdlrANz9A41swMxmAF8DLnP3vWXPBsetWmHeM2rZuPtaYC1EFVkaSUOe6ULXXkncZLTqAlgadyx+wb7gtIV6LtSEWs/IgZYFQ92sZlcjFVmeik13A79BVFRZl5l1EQW8L7r718PsITPrC7m8PmBXmL8dWBR7+0JgZyPb6WS60LVXEjcZrbwAFr3yw2RzadVy3Z+/9WFGxp5uWYUx3axmV90mC894g9l04EZ3P7vOekb0zO4Jd78sNv8jwOPufq2ZXQnMcfc/NbOTgC8RPcc7jqiSy4nufqjaNorSZEHaq9XFX2pq0BqtOI5XfOUu+nq7mRIrcXranR9sHuIlS5897sakdKNy+VnLJpzeLNYCLoLJNlko9yzguQ2s93LgbcA9ZnZnmPc+4FrgBjO7GHgEOB/A3e81sxuA+4hqfl5SK+CJJKXVuSnl1lujFc9Gq+W6DWt5hbGi58qzqpFnevdw5NnaVGAeUPd5nrv/B5Wf0wG8tsp7Pgh8sN5ni+SNLoCT14pno9WKHV+0qJd9owf1DK4AGsnp/UZs+iAw5O51G6eLiLRSK56NVst1A3oGVxBVg17oOQWgvInCLDPD3Z9ILlkiIuO1qnJItVy3iqCLoVZO73aiYs1qTQkaea4nItISST8bVRF0MVQNeu6+tJ0JERGpR4FJJquh2ptmdgxwIlE7PQDcfWNSiRIREUlCI7U3/xB4N1Fj8TuJOo++FXhNoikTERFpsUY6nH43cBrwsLu/mqgPzd2JpkpERCQBjQS9UXcfhag3Fnf/OfD8ZJMlIiLSeo0809tuZrOBbwIbzOxJ1CemiIjkUCNDC70pTF5jZj8CeoH1iaZKREQkAY1UZPkY8GV3/7G739KGNImIiCSikWd6dwBXm9mDZvYRM6vYc7WIiEjW1Q167r7O3c8hGvJnC/BhM3sg8ZSJiIi0WCM5vZLnAS8AlgA/TyQ1IiIiCaob9MyslLP7ALAJeLG7vzHxlImIiLRYI00WtgIvc/fHkk6MiEg7aFTz4mqkycI/tyMhkm+6iEhcls+HzYPDrN24ld6eLvp6uxkeGWPtxq2sPmNpZtIoyWnmmZ5IRaWLyPDI2LiLyObB4bSTJinI+vmwftMQvT1d9PZ0McXs8PT6TUNpJ03aoKFRFkRqiV9EgMP/128a0p1zAWX9fNixZ4S+3u5x82Z2T2PHnpGUUtRaWc5lZ0HVnJ6Zzan1185ESrbt2DPCzO7x90+ddBGR5mT9fFgwu4d9owfHzds3epAFs3tSSlHrZD2XnQW1ijdvBwbC/91EbfQeCNO3J580yYtOvohI87J+PqxaOZ/hkTGGR8Z42v3w9KqV89NO2qSp6La+qkHP3Ze6+3OB7wFvdPe57v5s4DeAr7crgZJ9nXwRkeZl/XxY3tfL6jOW0tvTxeDwKL09XR1TiSXruewsaOSZ3mnu/s7SC3f/rpn9VYJpkpwpXUTizxEuOG1hR1xEpHl5OB+W9/VmKj2tsmB2D8MjY4efo0K2ctlZ0EjQe8zMrga+ADjwVuDxRFMludOpF5FOkEbFBp0P6Vi1cj5rN24FohzevtGDDI+MccFpC1NOWXaYu9deIaq08n7gDKKgtxH4gLs/kXzyauvv7/eBgYG0k9FRslbzK2vpyZt4m7T4RbBTivPkmfSbATO73d0rDo5QN+jFPmSGu+9vacomSUGvtbJ2gcxaevJozYYtzyjuKr2+/KxlKaZMJDm1gl4jfW/+upndB9wXXr/QzD7e4jRKBmSt5lfW0pNHqtggMl4jPbKsAc4mPMdz97uIijqlw2TtApm19ORR1psPiLRbQ92Qufu2slmHEkiLpCxrF8ispSePst58QKTdGgl628zs1wE3s6PM7Apgc8LpkhRk7QKZtfTkUSe3SROZiEZqb84FPgacCRjwfeBS1d7sTFmr+ZW19IhI9tWqyNJIO73nu/vvln3gy4H/bEXiJFuy1r4qa+kRkXxrpHjzHxqcJyIikmlVc3pm9jLg14F5ZvY/Y4tmAVOTTpiIiEir1SrePAqYEdaZGZu/F3hzkokSERFJQtWg5+63ALeY2XXu/nAb0yQiIpKIRiqyfNrMznf3PQBmdgxwvbufnWjKRERyQrWM86ORiixzSwEPwN2fBI5NLEUiIjmi0crzpZGg97SZLS69MLPjiUZbEBEpPPURmy+NFG/+OfAfZnZLeH0GsDq5JImI5MeOPSP09XaPm6c+YrOrbtBz9/VmdirwUqIeWS5398cST5mISA5otPJ8qVq8aWYvCP9PBRYDO4EdwOIwT0Sk8NRHbL7Uyum9B3gH8LcVljnwmkRSJIlSLTOR1ip16h3/XV1w2kL9rjKq4ZHTs0gdTjdHI5GLSBFMqMNpM/utWh/q7l+fbMKkveK1zIDD/9dvGlLQE5FCqFW8+cbw/1iiPjh/GF6/GrgZUNDLGdUyE5Giq9UN2e8DmNm3gRXuPhhe9wH/1J7kSSuplpmIFF0jjdOXlAJeMAQsSyg9kiDVMhORomsk6N1sZt8zs7eb2UXAd4AfJZwuSUCplllvTxeDw6P09nSpEouIFEojjdPfZWZvIuqJBWCtu38j2WRJUjQSuYgUWSPdkAHcAexz9x+Y2bPMbKa770syYSJSTGpLKkmqW7xpZu8Avgp8MsxaAHyzgfd91sx2mdmm2LxrzGyHmd0Z/s6JLbvKzB40s/vNTMMWiRSQRiyQpDWS07sEOB34KYC7P2BmjQwtdB3wj8DnyuavcfePxmeY2QrgQuAk4DjgB2a2zN0PNbAdkZZSTiM9aksqSWukIssBd/9V6YWZTaOBoYXcfSPwRIPpOJdoYNoD7r4VeJAo0Iq0lXIa6dqxZ4SZ3ePvxdWWVFqpkZzeLWb2PqDHzM4C/gfwr5PY5rvM7PeAAeA9YVDaBcBPYutsD/OkgiznRLKctkYop5EutSWVpDWS0/szYDdwD/BHwL8BV09we58ATgBOAQY50pm1VVi3Ym7SzFab2YCZDezevXuCycivLOdEspy2RimnkS61JZWk1Qx6ZjYFuMfdP+Xu57v7m8P0hHqpdvchdz/k7k8Dn+JIEeZ2YFFs1YVEQxlV+oy17t7v7v3z5s2bSDJyLcujNGc5bY1aMLuHfaMHx81TTqN91JZUklazeNPdnzazu8xssbs/MtmNmVlfrHeXNwGlmp03Al8ys78jqshyInDbZLfXibLcf2aW09aoVSvns3bjVoBxI1FccNrClFNWHGpLKklq5JleH3Cvmd0GPFWa6e6/WetNZvYvwKuAuWa2HXg/8CozO4Wo6PIhouJS3P1eM7sBuA84CFyimpuVZfmZR5bT1iiNjSbS2eqOp2dmr6w0391vSSRFTSjieHpZHhMvy2kTkeKoNZ5e1aBnZt3AO4HnEVVi+Yy7H6y4ckqKGPQg2zUks5w2ESmGCQ0iC6wDxoB/B14PrADe3frkSbPSeubRSEDT85h80s2KFEWtnN497v5rYXoacJu7n9rOxNVT1JxeGjq16FIX+879bqW4auX0ajVZGCtNZK1YU9qvE5ojlOuEdoWt0InfrUg1tYLeC81sb/jbB5xcmjazve1KoGRDJzba1sU+0onfrUg1VZ/pufvUdiZEsq3UHGHs0CEe3PUUe0fHOGrqFFYeNyuV9LSiWLIT2hW2Qp6bmqh4WprVSDdkIqxaOZ9tT/ySW3/xBCO/OshRU4z9owfZOTza9uLAVhVLqveVSF67/lLxtEyEgp40ZHlfL/NnTWdm9zQOPg3Tj5rGS547h+OffXTbiwNbVSyZ14t9q+W16y8VT8tENDpyugi/OuScsWweU+xI/+BPu7e9OLBVxZLqfeWIPDY1UfG0TISCnjQsK89+WpmOPF7sJZKV81HyRcWb0rCsFAdmJR2SLp0HMhF1+97MMjVOb7+s1JbLSjqksnZ9PzoPpJIJ9b2ZBwp6ItmjHl4kbRPtkUVEpGmqVSlZpqAnIi2lHl4kyxT0RKSl1OhfskxBT0RaSrUqJcsU9ESkpfLaw4sUgxqnt5CqT4tE1Ohfsko5vRZR57ciItmnoNciqqYtIpJ9CnotomraIiLZp6DXIqqmLSKSfQp6LaJq2iIi2aeg1yKqpi0ikn1qstBCqqYtIpJtyumJiEhhKKeXE2r43jwds+p0bKSolNPLATV8b56OWXU6NlJkCno5oIbvzdMxq07HRopMQS8H1PC9eTpm1enYSJEp6OWAGr43T8esOh0bKTIFvRxQw/fm6ZhVp2MjRaaglwNq+N48HbPqdGykyMzd007DhPX39/vAwEDayWi7NKubq6q7iGSdmd3u7v2VlimnlzNpVjdXVXcRyTsFvZxJs7q5qrqLSN4p6OVMmtXNVdVdRPJOQS9n0qxurqruIpJ3Cno5k0Z1882Dw6zZsIV7dw7zk/96nIce26+q7iKSSwp6OdPu6ubxyivL+2ax7NgZ3P/ofn7+6F5VdReR3NEoCznUznH74pVXAJbOm8GcGdPp7eni8rOWtSUNIiKtopye1KTKKyLSSRT0pCZVXhGRTqKgJzWpn0YR6SQKelKT+mkUkU6iiixSVzsrzoiIJEk5PRERKQwFPRERKQwFPRERKQwFPRERKQwFPRERKYzEgp6ZfdbMdpnZpti8OWa2wcweCP+PiS27ysweNLP7zezspNIlIiLFlWRO7zpgVdm8K4Gb3P1E4KbwGjNbAVwInBTe83Ezm5pg2kREpIASC3ruvhF4omz2ucC6ML0OOC82/3p3P+DuW4EHgdOTSpuIiBRTu5/pzXf3QYDw/9gwfwGwLbbe9jDvGcxstZkNmNnA7t27E02siIh0lqxUZLEK87zSiu6+1t373b1/3rx5CSdLREQ6Sbu7IRsysz53HzSzPmBXmL8dWBRbbyGws81pE0nc5sFh1m8aYseeERbM7mHVyvnq4k2kjdqd07sRuChMXwR8Kzb/QjObbmZLgROB29qcNiG6KK/ZsIUrvnIXazZsYfPgcNpJ6hjxUej7ersZHhlj7catOsYibZRkk4V/AW4Fnm9m283sYuBa4CwzewA4K7zG3e8FbgDuA9YDl7j7oaTSJpXpopys+Cj0U8wOT6/fNJR20kQKI7HiTXf/nSqLXltl/Q8CH0wqPVJf/KIMHP6/ftOQiuBaYMeeEfp6u8fN0yj0Iu2loYXksKQvykV/nrVgdg/DI2OHbyZAo9CLtFtWam9KBiyY3cO+0YPj5rXqoqyiU41CL5IFyumlpDzXs2z+0WwZeirVXNCqlfNZu3ErEOXw9o0eZHhkjAtOWzjpz1bR6ZFR6OPf+wWnLSzM/otkgYJeCkq5nt6eLvp6u3nosf18/Y7tvGjRbI6fe/ThXNDqM5a29YKY5EVZz7MiGoVeJF0Keikoz/U8uvcAR0+fxqP7DrB03oxUc0FJXZT1PEtEskDP9FKwY88IM7uP3G/sHR1j5vSp7I89T+u0XJCeZ4lIFiinl4LyXM+s7i6GR8aYNclcUJZrR+p5lohkgYJeCsorjDxn1nR27hlh2bEzeNp9QhVIyp8TpvVcsBY9zxKRtCnopaA817Nk7gxed9L8cbU3m80FqXaktFKWSw1EJkNBLyWVcj1vmMTnqXaktEoeSg1EJkoVWTpEkg3LpVjUR6h0MgW9DqHakdIq5bWLQaUG0jlUvNkhGq0dqWc1Uo/aVEonU9DrIPVqR+pZjTQiye7oRNKm4s0C0bMaaUSp1KC3p4vB4VF6e7p0YyQdQzm9AlENT2mU2lRKp1JOr0BUw1NEik5Br0BUw1NEik5Br0D0rEZEik7P9Aqm/FnN5sFh1mzYoiYMIlIIyukVWKkJw/DI2LgmDJsHh9NOmohIIpTTa6G8NfxWJ9UiUjQKelU0G8Dy2PBbTRhEpGhUvFnBRIr98tjwW00YRKRoFPQqmEgAy2MnvWrCICJFU+jizWpFmBMp9ptMJ71pPQss76T6qKnGs7qm8Jn/eCgXzyRFRJpV2JxerSLMiRT7TTTXlHYNyuV9vVx+1jIufsUSRsaepmva1KbSUWrycMVX7mLNhi2q+SkimVbYoFerCHMiAWyiDb+z8ixwIulIO2CLiDSrsMWbtYowGx2brtxEOunNSg3KiaRDTR5EJG8KG/TqPYNrVy/zWRmwcyLpyErAFhFpVGGLN7NSczHP6VCTBxHJm8IGvax0vpzndGQlYIuINMrcPe00TFh/f78PDAyknYxCm2hzi7x12SYi+WFmt7t7f6VlhX2mlyV5DgATefaZxy7bRKQzFLZ4MyuKWO0/K800RKR4lNNLQTxn98gTv+Q5M6cXqtq/an2KSFqU02uz8pzd4/sPsGVoP7v3jR5ep9MDgGp9ikhaFPTarLxob+6M6WDw4O6nDq/T6QFAtT5FJC0Kem1WPhrD8449Gnfnif2/KkwAyEozDREpHj3Ta7Pynk/mzujmBc+ZyeDeAwwOjx7u8gxgzYYtuazR2Yh29XgjIhKnnF6bVSramzJlCn/5myv46Pkv5PKzlgEUrkaniEg7KKc3Ad+5ewfrbn2Eob2jzJ/VzUUvW8wbTl7Q0Hsb6cw6zY6c89xmUESkHgW9Jn3n7h1c+937OXr6NI6dcRR7R8a49rv3AzQV+GoFkrSq9KvReOfSzYxIRMWbTVp36yMcPX1aVPtyyhR6e7o4evo01t36SMu2kVaVfjUab50sDa5bxA4QRKpR0GvS0N5RZk6fOm7ezOlTGdo7WuUdzUurSn95zVLo/DaDSchakNHNjMgRCnpNmj+rm30HDo2bt+/AIebP6q7yjualVaVfjcZbI2tBRjczIkfomV6TLnrZ4sPP8GZOn8q+A4d46sBB/uQ1J7R0O2lU6V+1cj5rN24FoovivtGDDI+MHW5CIY3JWjdrWRmoWCQLFPSaVKqsEq+9+SevOaHhSixZ1kjN0k6SVOWOrAUZ3cyIHKHx9KQtslZ7MF5TNR4IWlGM3OrPbsWxy9rxF0lSrfH0FPRkQpq5iCYZYCaatjUbtjwjN1Z6XeogIOk0NPo57Tx2Ip1Ag8hKSzXbnq9aY/sv3Powc2d2tzT30Wjakn7u1qpnsml2VCDSiVKpvWlmD5nZPWZ2p5kNhHlzzGyDmT0Q/h+TRtqkvmZrJ1aqPXjg4EH+/cHHW16tv9G05aWmqmpeirRWmk0WXu3up8SyoFcCN7n7icBN4bVkULMX4koB5r6d+zjmWa2v1t9o2vIyvFFegrNIXmSpnd65wLowvQ44L72kZEOWevWIa/ZCXCnAPPnLMZb3zTy8zu59o9y3c5hv3rljUvvaaNryMrxRXoKzSF6kUpHFzLYCTwIOfNLd15rZHnefHVvnSXevWcTZyRVZslyBYSJpK6/YsXvfKEdNm0pvTxe7941yxyN7AJjVPY0Vx/VOeF+zfNwmSjUvRZqTudqbZnacu+80s2OBDcCfADc2EvTMbDWwGmDx4sUvfvjhh9uU6vZKunbhZE32QhwPTvftHGZvyJ29+PjZzJ3RPal9VZAQKbbM1d50953h/y4z+wZwOjBkZn3uPmhmfcCuKu9dC6yFKKfXrjS3W9Z69Sg32dqJ8YbwQ/sOMH/mdE6cP4O5M6J9nsy+aoBaEamm7UHPzI4Gprj7vjD9OuADwI3ARcC14f+32p22LMlarx5JiAenTt9XEcmGNHJ684FvmFlp+19y9/Vm9jPgBjO7GHgEOD+FtGVGra6jOq34Tt1kiUi7qEeWDKsU3ICOq6gBeg4nIq2TuWd60phKz6bWbNjSkT106DmciLRDltrpSQPUQ4eIyMQp6OWMeugQEZk4Bb2cUQ8dIiITp6CXM3npPktEJItUkSWHVOlDRGRilNMTEZHCUNATEZHCUNATEZHCUNATEZHCUNATEZHCUNATEZHCUNATEZHCUNATEZHCUNATEZHCUI8sItISGhNR8kA5PRGZtM2Dw6zduJXhkTH6ersZHhlj7catbB4cTjtpIuMo6InIpK3fNHR4cOMpZoen128aSjtpIuMo6InIpGlwY8kLBT0RmTQNbix5oaAnIpOmwY0lLxT0RGTSNLix5IWaLIhIS2hwY8kD5fRERKQwFPRERKQwFPRERKQwFPRERKQwFPRERKQwFPRERKQwFPRERKQwFPRERKQwFPRERKQwFPRERKQwFPRERKQwFPRERKQwzN3TTsOEmdlu4OG00xHMBR5LOxEp0zHQMQAdA9AxgHSPwfHuPq/SglwHvSwxswF37087HWnSMdAxAB0D0DGA7B4DFW+KiEhhKOiJiEhhKOi1ztq0E5ABOgY6BqBjADoGkNFjoGd6IiJSGMrpiYhIYSjoiYhIYSjoTYCZPWRm95jZnWY2EObNMbMNZvZA+H9M2ulsNTP7rJntMrNNsXlV99vMrjKzB83sfjM7O51Ut1aVY3CNme0I58OdZnZObFlHHQMzW2RmPzKzzWZ2r5m9O8wvzHlQ4xgU6TzoNrPbzOyucAz+MszP/nng7vpr8g94CJhbNu9vgCvD9JXAh9NOZwL7fQZwKrCp3n4DK4C7gOnAUuAXwNS09yGhY3ANcEWFdTvuGAB9wKlheiawJexnYc6DGsegSOeBATPCdBfwU+CleTgPlNNrnXOBdWF6HXBeeklJhrtvBJ4om11tv88Frnf3A+6+FXgQOL0d6UxSlWNQTccdA3cfdPc7wvQ+YDOwgAKdBzWOQTWdeAzc3feHl13hz8nBeaCgNzEOfN/Mbjez1WHefHcfhOhHARybWuraq9p+LwC2xdbbTu0LQ969y8zuDsWfpSKdjj4GZrYEeBHRXX4hz4OyYwAFOg/MbKqZ3QnsAja4ey7OAwW9iXm5u58KvB64xMzOSDtBGWQV5nVq+5hPACcApwCDwN+G+R17DMxsBvA14DJ331tr1QrzOvUYFOo8cPdD7n4KsBA43cxW1lg9M8dAQW8C3H1n+L8L+AZRNn3IzPoAwv9d6aWwrart93ZgUWy9hcDONqetLdx9KFwAngY+xZFim448BmbWRXSx/6K7fz3MLtR5UOkYFO08KHH3PcDNwCpycB4o6DXJzI42s5mlaeB1wCbgRuCisNpFwLfSSWHbVdvvG4ELzWy6mS0FTgRuSyF9iSv9yIM3EZ0P0IHHwMwM+Ayw2d3/LraoMOdBtWNQsPNgnpnNDtM9wJnAz8nDeZB2LaC8/QHPJaqFdBdwL/DnYf6zgZuAB8L/OWmnNYF9/xeiYpsxoju3i2vtN/DnRLW07gden3b6EzwGnwfuAe4m+nH3deoxAF5BVCx1N3Bn+DunSOdBjWNQpPPgZOD/hX3dBPxFmJ/580DdkImISGGoeFNERApDQU9ERApDQU9ERApDQU9ERApDQU9ERApDQU8kYWb27FjP+4+W9cR/VEpputnM+tPYtkiapqWdAJFO5+6PE3VNhZldA+x394+WlpvZNHc/mE7qRIpFOT2RFJjZdWb2d2b2I+DDYSy2K2LLN4XOjDGzt4axy+40s0+a2dSyz3q9md0Qe/0qM/vXMP0JMxuIj3lWIS37Y9NvNrPrwvQ8M/uamf0s/L28hYdAJBUKeiLpWQac6e7vqbaCmS0HLiDq5PwU4BDwu2WrbQBeGrrFI6z/5TD95+7eT9SDxivN7OQm0vcxYI27nwb8NvDpJt4rkkkq3hRJz1fc/VCddV4LvBj4WdTlIz2UdWbu7gfNbD3wRjP7KvAG4E/D4reE4a+mEQ1+uoKo66hGnAmsCNsFmGVmMz0aQ04klxT0RNLzVGz6IONLXrrDfwPWuftVdT7ry8AlRAPc/szd94WOfa8ATnP3J0OxZXeF98b7IowvnwK8zN1H6u6JSE6oeFMkGx4CTgUws1OBpWH+TcCbzezYsGyOmR1f4f03h/e/gyNFm7OIAuuwmc0nGv+xkiEzW25mU4hGByj5PvCu0gszO6XpvRLJGAU9kWz4GjAnjET9x8AWAHe/D7ga+L6Z3U30/K6v/M2hmPTbRIHt22HeXUQ94d8LfBb4zyrbvjK854dEI0iUXAr0h5HA7wPeObldFEmfRlkQEZHCUE5PREQKQ0FPREQKQ0FPREQKQ0FPREQKQ0FPREQKQ0FPREQKQ0FPREQK4/8DCUUJ3E5EwMoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bmi\n",
      "Train score: 1.0\n",
      "Test score: -0.3383403635171347\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 7091 \n"
     ]
    },
    {
     "data": {
      "image/png": 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NPFtvLpK0MJ3uAU4HbpPUl1ntZcDGdPpq4FxJB0haDhwHXJ9X+hpt0+AwazdsZnhklL7eboZHRlm7YTObBodbnbRZ6dT9Mmsl/65aJ887vT7gCklzSYLrVRHxTUmflXQySdHl3cDrASLiFklXAbcCe4Hzi9Ryc93GIXp7uujt6QIY/79u41Chr946db/MWsm/q9bJLehFxM3AM6vMf02N91wMXJxXmvK0dccIfb3dE+Yt6J7H1h0jLUpRY3Tqfpm1kn9XreMRWRpkycIedu7eO2Hezt17WbKwp0UpaoxO3S+zVvLvqnUc9Bpk9crFDI+MMjwyyuMR49OrVy5uddJmpVP3y6yV/LtqHQe9BlnR18ua05bT29PF4PBuenu6WHPa8sKXz3fqfpm1kn9XraOIWoOktLf+/v4YGBhodTLMzKyNSLohIvqrLfOdnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlYaDnpmZlca8VifAzMyaY9PgMOs2DrF1xwhLFvaweuViVvT1tjpZTeU7PTOzEtg0OMzaDZsZHhmlr7eb4ZFR1m7YzKbB4VYnrakc9MzMSmDdxiF6e7ro7elijjQ+vW7jUKuT1lQOemZmJbB1xwgLuifWaC3onsfWHSMtSlFrOOiZmZXAkoU97Ny9d8K8nbv3smRhT4tS1BoOemZmJbB65WKGR0YZHhnl8Yjx6dUrF7c6aU3loGdmVgIr+npZc9pyenu6GBzeTW9PF2tOW1661pvusmBmVhIr+npLF+Qq+U7PzMxKw3d6Ni3u3GpmReY7PaubO7eaWdHlFvQkdUu6XtJNkm6R9J50/qGS1ku6M/1/SOY9F0q6S9Ltks7MK202M+7camZFl+ed3h7gBRHxDOBkYLWkZwPvAK6JiOOAa9LXSDoROBc4CVgNfFTS3BzTZ9Pkzq1mVnS5Bb1I7EpfdqV/AZwNXJHOvwJ4aTp9NnBlROyJiM3AXcCpeaXPps+dW82s6HKt05M0V9KNwDZgfUT8DFgcEYMA6f/D09WXAFsyb783nVf5mWskDUga2L59e57Jtwru3GpmRZdr0IuIfRFxMrAUOFXSyhqrq9pHVPnMtRHRHxH9ixYtalBKrR7u3GpmRdeULgsRsUPStSR1dUOS+iJiUFIfyV0gJHd2R2bethS4rxnps/q5c6uZFVmerTcXSVqYTvcApwO3AVcD56WrnQd8I52+GjhX0gGSlgPHAdfnlT4zMyufPO/0+oAr0haYc4CrIuKbkn4CXCXptcA9wCsBIuIWSVcBtwJ7gfMjYl+O6TMzs5JRxH7VZoXR398fAwMDrU6GmZm1EUk3RER/tWUehsysQWoN0ebh22w6nF/y42HIzBqg1hBtHr7NpsP5JV++0zNrgOwQbcD4/7Eh2iZb5qt3q1QrLzm/zJ7v9MwaoNYQbR6+zabD+SVfDnpmDVBriDYP32bT4fySLwc9swaoNUSbh2+z6Th+8UH89FcP8q2bB/nJrx5g8/Zdzi8N5KBn1gC1hmjz8G1Wr02Dw3x/03ZOeMp8Dj2oiwcffYw7tu3i9BWLnF8axA1ZzBqk1hBtHr7N6pFtxLLssPkADI+McsfQo/xBi9PWKXynZ2bWJtyIJX8OemZmbcKNWPLnoGdm1ibc6Cl/rtMzM5ulRg0bNtboKftZ56xa6vrgBnLQMzObhbFhw3p7uiYMGzbTFrpu9JQvF2+amc1CtsXlHGl8emwIOmsvDnpmZrPgFpfF4uJNs4Ly42faw5KFPQyPjI4PDA1ucdnOfKdnVkB+/Ez7cIvLYnHQMysg1yO1Dw8zVywu3jQroK07Rujr7Z4wz/VIreMWl8XhOz2zAvLIHWYz4zs9swJavXIxazdsBpI7vJ279zI8Mso5q5a2OGXWas1u4FS0BlW+0zMrINcjWTXNbuBUxAZVvtOzlija1WE7cj3SzHRy3ss2cALG/6/bOJTLPjZ7e43gOz1ruiJeHVpn6PS81+yO8kXsmO+gZ03n5vbWKp2e95rdwKmIDaoc9Kzpinh1aJ2h0/NeszvKF7FjvoOeNV0Rrw6tM3R63mt2A6ciNqhyQxZrOje3t1YpQ95rdgOnojWo8p2eNV0Rrw6tMzjvme/0rCWKdnVoncN5r9x8p2dmZqXhoGdmZqXhoGdmZqXhoGdmZqXhoGdmZqXhoGdmZqXhoGdmZqXhoGdmZqXhoGdmZqXhoGdmZqXhYcjMCqSTn/pt1gy+0zMriE5/6rdZMzjomRVEpz/126wZHPTMCqLTn/pt1gwOemYF0elP/TZrBjdkMSuIsad+P7RrD/c/spuHHh1l3hzxP154TKuTZlYYvtMzK4gVfb2cvmIRd2zbxYOPPsahB3VxwlPm8/1N292YxaxOvtMzK5A7hh7l2U99Mr09XePzhkdGWbdxyF0XzOqQ252epCMl/VDSJkm3SHpzOv8iSVsl3Zj+nZV5z4WS7pJ0u6Qz80qbWVG5MYvZ7OR5p7cXeFtE/ELSAuAGSevTZZdGxAezK0s6ETgXOAk4Avi+pOMjYl+OaTQrlCULexgeGZ1wp+fGLGb1y+1OLyIGI+IX6fROYBOwpMZbzgaujIg9EbEZuAs4Na/0mRXR6pWLGR4ZZXhklMcjxqdXr1zc6qSZFUJTGrJIWgY8E/hZOuuNkm6W9GlJh6TzlgBbMm+7lypBUtIaSQOSBrZv355nss3azoq+Xtactpzeni4Gh3fT29PFmtOWuz7PrE65N2SRNB/4CvCWiHhE0seAvwci/f8h4C8AVXl77DcjYi2wFqC/v3+/5WadbkVfr4Oc2QzleqcnqYsk4H0+Ir4KEBFDEbEvIh4HLuOJIsx7gSMzb18K3Jdn+szMrFzybL0p4FPApoj4cGZ+X2a1lwEb0+mrgXMlHSBpOXAccH1e6TMzs/LJs3jzucBrgF9KujGd907gVZJOJim6vBt4PUBE3CLpKuBWkpaf57vlppmZNVJuQS8ifkT1erpv13jPxcDFeaXJzMzKzSOymJnlwA/8bU8ee9PMrMH8wN/25aBnZtZgfuBv+3LQMzNrMI+R2r4c9MzMGswP/G1fDnpmZg3mMVLbl4OemVmDeYzU9uUuC2bW8VrRfcBjpLYnBz3bj/sXWScZ6z7Q29M13n3g/etu54jebvbsC+fxknHxpk3g/kXWaSq7Dzy2dx/3PPhbNt73iPN4CTno2QTuX2SdprL7wF3bH2X+AXN5bN/jzuMl5KBnE7h/kXWayu4Du3bvJYCDu7vG5zmPl4eDnk3g/kXWaSq7D3TNFbv27OPYww8aX8d5vDzckKVkpmqksnrlYtZu2AwkV787d+9leGSUc1YtbVWSzWZlrPvAWL4/6YiDGXpkD11z5/J4hPN4ySgiWp2GGevv74+BgYFWJ6Mwsq3YsgGtsv+QW29ap3Me72ySboiI/mrLfKdXItlGKsD4/3Ubhyb84N2/yDqd83h5uU6vRNxIxczKzkGvRNxIxczKzkGvRDwIrpmVnev0OshUlfOVrdiWLOzhnFVLXbdhZtNW1MZAbr3ZIeptmWlmNlvtfr6p1XrTxZsdwsOHmVmzFPl8M2XQk3S8pGskbUxfP13Su/NPmk2HW2aaWbMU+XxTz53eZcCFwChARNwMnJtnomz63DLTzJqlyOebeoLegRFxfcW8vVXXtJZxy0wza5Yin2/qCXoPSDoGCABJrwAGc02VTdtYy8zeni4Gh3fT29PVNpXKZtZZiny+qafLwvnAWuBpkrYCm4FX55oqmxEPrWRmzVLU882UQS8ifg2cLukgYE5E7Mw/WWZmZo03ZdCT9DcVrwGIiL/LKU1mZma5qKd489HMdDfwh8CmfJJjZmaWn3qKNz+UfS3pg8DVuaXIzMwsJzMZkeVA4KmNToiZmVne6qnT+yVpdwVgLrAIcH2emZkVTj11en+Ymd4LDEWEO6ebmVnhTBr0JB2aTlZ2UThYEhHxUH7JMjMza7xad3o3kBRrqsqywPV6ZmZWMJMGvYhY3syEmJmZ5a2uJ6dLOgQ4jqSfHgARsSGvRJmZmeWhntabfwm8GVgK3Ag8G/gJ8IJcU2ZmZtZg9fTTezOwCvhNRPw+8Exge66pMjMzy0E9QW93ROwGkHRARNwGnJBvsszMzBqvnjq9eyUtBL4OrJf0MHBfnokyMzPLQz1jb74snbxI0g+BXmBdrqkyMzPLQT0NWT4CfDEifhwR1zUhTWZmZrmop07vF8C7Jd0l6QOS+vNOlJmZWR6mDHoRcUVEnAWcCtwBvE/SnbmnzMzMrMGm82ihY4GnAcuA23JJjZmZWY7qqdN7H/By4FfAF4G/j4gddbzvSOAzwFOAx4G1EfGRdCDrL5IEz7uBP4mIh9P3XAi8FtgHvCkivjv9XbIi2jQ4zLqNQ2zdMcKShT2sXrmYFX29rU6WmXWYeu70NgPPiYjVEfH/6gl4qb3A2yJiBckoLudLOhF4B3BNRBwHXJO+Jl12LnASsBr4qKS509obK6RNg8Os3bCZ4ZFR+nq7GR4ZZe2GzWwaHG510sysw9RTp/fxiHhguh8cEYMR8Yt0eiewCVgCnA1cka52BfDSdPps4MqI2BMRm4G7SOoRrcOt2zhEb08XvT1dzJHGp9dtHGp10sysw0ynTm/GJC0jGb7sZ8DiiBiEJDACh6erLQG2ZN52bzqv8rPWSBqQNLB9u0dD6wRbd4ywoHtiSfuC7nls3THSohSZWafKPehJmg98BXhLRDxSa9Uq82K/GRFrI6I/IvoXLVrUqGRaCy1Z2MPO3XsnzNu5ey9LFva0KEVm1qnqeXJ6VfU8OV1SF0nA+3xEfDWdPSSpLyIGJfUB29L59wJHZt6+FA93VgqrVy5m7YbNQHKHt3P3XoZHRjln1dIWp6wztKKRUF7bdIMnm61ad3o3AAPp/+0kffTuTKdvmOqDJQn4FLApIj6cWXQ1cF46fR7wjcz8cyUdIGk5yfP7rq9/V6yoVvT1sua05fT2dDE4vJveni7WnLbcJ7MGaEUjoby26QZP1ghTPjld0seBqyPi2+nrFwOn1/HZzwVeA/xS0o3pvHcClwBXSXotcA/wynR7t0i6CriVpOXn+RGxbyY7ZcWzoq/XQS4H2UZCwPj/dRuHcjveeW2zFftinaeepyysiog3jL2IiO9I+vup3hQRP6J6PR3ACyd5z8XAxXWkyczqsHXHCH293RPm5d1IKK9ttmJfrPPU05DlAUnvlrRM0tGS3gU8mHfCzGz2WtFIKK9tusGTNUI9Qe9VwCLga+nfonSembW51SsXMzwyyvDIKI9HjE+vXrm4cNtsxb5Y51HEfr0Cqq8ozY+IXTmnZ1r6+/tjYGCg1ckwa2tuvWllI+mGiKj6RKApg56k/wR8EpgfEUdJegbw+oj4741P6vQ46JmZWaVaQa+e4s1LgTNJ6/Ei4ibgtMYlz8zMrDnqGpElIrZUzHJXAjMzK5x6uixsSYs4Q9KTgDeRDB5tZmZWKPXc6b0BOJ9k8Od7gZOBltfnmZmZTVc9d3onRMSfZmdIei7w7/kkyczMLB/13On9U53zzMzM2lqtpyw8B/hPwCJJ/zOz6GDATzQ3M7PCqVW8+SRgfrrOgsz8R4BX5JmoonLHWTOz9lbrKQvXAddJujwiftPENBXS2GNPenu6Jjz2pJ5H5DhYmpk1Rz11ep+UtHDshaRDJH03vyQVU/axJ3Ok8el1G4dqvs/PCDMza556Wm8eFhE7xl5ExMOSDs8vScU008ee+BlhZjPXrFISl8Z0jnru9B6XdNTYC0lHA/WNUl0iM33sydYdIyzonnjt4WeEldemwWEuXX8HF3zpJi5df4fv+GtoVimJS2M6Sz1B713AjyR9VtJngQ3Ahfkmq3hm+tgTPyPMxvjkOj0zrVJo1+1Yc0wZ9CJiHXAK8EXgKuB3I8J1ehVW9PWy5rTl9PZ0MTi8m96erroasfgZYTbGJ9fpaVYpiUtjOkutfnpPi4jbJJ2Szrov/X+UpKMi4hf5J69YVvT1TrucfyxYZusLzlm11PUFJTTTeuGyWrKwh+GR0fF6cMinlKRZ27HmqNWQ5W3A64APVVkWwAtySVEJzSRYWufxyXV6Vq9czNoNm4Hk4mDn7r0Mj4xyzqqlhdyONUfdT05vR36IrHWSbF/P7Mm1nmLysnLrTatmRk9Ol/TyWh8aEV9tQNpmxUHPOo1PrmazVyvo1SrefEn6/3CSMTh/kL7+feBaoOVBz6zTuKjbLF+1hiH7rwCSvgmcGBGD6es+4J+bkzwzM7PGqaef3rKxgJcaAo7PKT1mZma5qWcYsmvTsTa/QNJq81zgh7mmyszMLAdTBr2IeKOklwGnpbPWRsTX8k2WmZlZ49VzpwfwC2BnRHxf0oGSFkTEzjwTZmZm1mhT1ulJeh3wZeAT6awlwNdzTJOZmVku6mnIcj7wXJInphMRd5J0YzAzMyuUeoLenoh4bOyFpHn40UJmZlZA9QS96yS9E+iRdAbwJeBf802WmZlZ49UT9N4ObAd+Cbwe+Dbw7jwTZWZmloearTclzQFujoiVwGXNSZKZmVk+at7pRcTjwE2SjmpSeszMzHJTTz+9PuAWSdcDj47NjIg/yi1VVjh+OoCZzVQzzx/1BL335LJl6xjZ58D19XYzPDLK2g2b/Rw4M5tSs88fkwY9Sd3AG4BjSRqxfCoi9jY8BVZ46zYO0dvTNf7E77H/6zYOOeiZWU3NPn/UqtO7AugnCXgvBj7U8K1bR9i6Y4QF3ROvnxZ0z2PrjpEWpcjMiqLZ549axZsnRsTvAEj6FHB9LimwwluysIfhkdHxKzSAnbv3smRhTwtTZWZF0OzzR607vdGxCRdrWi2rVy5meGSU4ZFRHo8Yn169cnGrk2Zmba7Z549aQe8Zkh5J/3YCTx+blvRILqmxQlrR18ua05bT29PF4PBuenu63IjFzOrS7POHIoo7jGZ/f38MDAy0OhmW4a4LZtZqkm6IiP5qy+oZhsysLmNNj4dHRic0Pd40ONzqpJmZAQ561kDZpsdzpPHpdRuHWp00MzPAQc8ayF0XzKzd1TMii1ld3HXBGsH1wpan3O70JH1a0jZJGzPzLpK0VdKN6d9ZmWUXSrpL0u2SzswrXZafTum6sGlwmEvX38EFX7qJS9ff0dA6yTw/uxO4Xtjylmfx5uXA6irzL42Ik9O/bwNIOhE4Fzgpfc9HJc3NMW2Wg07oupDnSdcn9Km5XtjyllvxZkRskLSsztXPBq6MiD3AZkl3AacCP8krfZaPFX29hQpylfIcB9BjlE5t644R+nq7J8xzvbA1Uivq9N4o6c+AAeBtEfEwsAT4aWade9N5+5G0BlgDcNRR5XnMX171HK4/mSjPk65P6FNzvbDlrdmtNz8GHAOcDAzyxCDWqrJu1V7zEbE2Ivojon/RokW5JLLd5FUs5uK2/S1Z2MPO3RNH3WvUSTfPz+4UnVIvbO2rqUEvIoYiYl/6RPbLSIowIbmzOzKz6lLgvmamrZ3lVc/h+pP95XnS9Ql9ap1QL2ztranFm5L6ImIwffkyYKxl59XAv0j6MHAEcBx+qsO4vIrFXNy2v7GTbrbI95xVSxty0s3zsztJ0euFrb3lFvQkfQF4PnCYpHuBvwWeL+lkkqLLu4HXA0TELZKuAm4F9gLnR8S+vNJWNHnVc0z3c13/N3s+oZu1Vm7FmxHxqojoi4iuiFgaEZ+KiNdExO9ExNMj4o8yd31ExMURcUxEnBAR38krXUWUV7HYdD63LPV/ZdlPs7LyMGQFkFc9x3Q+tyz1f2XZT7Oy8jBkBZFXsVi9n1uW+r+y7KdZWflOz+pSlub2ZdlPs7Jy0LO6lKW5fVn206ysHPSsLmXpP1WW/TQrK9fpWd3K0ty+E/dzut1N3D3FOpXv9Mw63HS7YbR7tw0/nslmw0HPrMNNtxtGO3fbaPeAbO3PQc+sw23dMcKC7ok1GbW6YUx3/WZq54BsxeCgZ9bhptsNo527bbRzQLZicNAz63DT7YbRzt022jkgWzE46Jl1uOl2w2jnbhvtHJCtGBRR9VmthdDf3x8DAwOtToaZNZG7U9hUJN0QEf3VlrmfnpkVSif2o7TmcfGmmZmVhoOemZmVhoOemZmVhuv0rK250YKZNZKDnrWtsSGnenu6Jgw51S7N5624fDFVXi7etLblIacsDx6/s9x8p2cTtNMV8NYdI/T1dk+Y5yGnbLayF1PA+P91G4d8t1cCDno2btPgMB/87h08sGsPe/bu486hnWzcOswFZx7fkpPBkoU9DI+Mjp+UwENO2ez5YqrcHPRs3Od+8hs2P/AoC7rncXB3F3v2Ps7mBx7lcz/5DRe//OlNT8/qlYtZu2EzkJyUdu7ey/DIKOesWtr0tHSydrq7b4ZOu5gq2/c3W67Ts3H/sWWY+QfMpbtrLpLo7prL/APm8h9bWlPX0c5jQHaKMtZvddL4nWX8/mbLd3o2LgiEJsxTOr9VPORUvspYvzV2MZW9Ozpn1dJC7m8Zv7/ZctCzcc88ciE/+/VDIHHAvDns2fs4u/bs41lPPbTVSbOclLV+q1Mupsr6/c2Gizdt3GueczRHPflAgPFnlh315AN5zXOObmWyLEd+Pl2x+fubPt/p2bgVfb389eoTcqkUd2V7e3JjoWLz9zd9fp6e5S47skr2h+lGKe3BFyTF5u9vf36enrWUK9vbW6fUb5WVv7/pcZ2e5W7rjhEWdE+8vnJlu5m1gu/0LHed1hm42Vx8ZdY4vtOzcZsGh7l0/R1c8KWbuHT9HQ3r4NpJnYGbzZ2PzRrLQc+AfE+uY52BH9u7j+9vGuL6zQ9xYJezXj38pAmzxvKZx4DmnFxHRh/nWcufzAtXHE7XvLm+Y6mD60PNGstBz4D8T66+Y5kZdz42aywHPQPyP7n6jmVmXB9q1lgOegbkf3L1HcvM+EkTZo3lLgsG5D/yfKOHSypTM353PjZrHA9DZk3TqEDlYc3MrBYPQ2ZtoVF3LB7WzMxmynV6VjhuFGNmM+WgZ4XjRjFmNlMu3rSWmWkdn58hZmYz5Ts9a4nZDHvmZvxmNlO+07OWmG1jFDfjN7OZcNCbRJn6gbXC1h0j9PV2T5jnxijVOS+aNY6LN6vw41zy58Yo9XFeNGus3IKepE9L2iZpY2beoZLWS7oz/X9IZtmFku6SdLukM/NKVz08OHL+PKZkfZwXzRorzzu9y4HVFfPeAVwTEccB16SvkXQicC5wUvqej0qam2PaanI/sPy5MUp9nBfNGiu3Or2I2CBpWcXss4Hnp9NXANcCb0/nXxkRe4DNku4CTgV+klf6almysIfhkdHxxhXgorc8uDHK1JwXzRqr2Q1ZFkfEIEBEDEo6PJ2/BPhpZr1703n7kbQGWANw1FFH5ZLIdu8H5oYN5ZFXXnQesrJql4YsqjKv6kjYEbE2Ivojon/RokW5JKadi97csKFc8siLzkNWZs2+0xuS1Jfe5fUB29L59wJHZtZbCtzX5LRN0K5Fbx5suXwanRedh6zMmn2ndzVwXjp9HvCNzPxzJR0gaTlwHHB9k9NWCG7YYLPlPGRllmeXhS+QNEQ5QdK9kl4LXAKcIelO4Iz0NRFxC3AVcCuwDjg/IvbllbYic/82my3nISuz3IJeRLwqIvoioisilkbEpyLiwYh4YUQcl/5/KLP+xRFxTEScEBHfyStdRef+bTZbzkNWZu3SkMXq1M6NbKwYnIeszDz2ZgG1ayMbKw7nISsr3+mZmVlpOOiZmVlpuHjTSivPUUmKNuJJ0dJrNlO+07NSynNUkqKNeFK09JrNhoOelVKej+wp2uOAipZes9lw0LNSynNUkqKNeFK09JrNhuv0Cmg29S+uu0nk+cieoj0OKI/0Op9Zu/KdXsHMpv7FdTdPyHNUkqKNeNLo9DqfWTtz0CuY2dS/uO7mCXmOSlK0EU8anV7nM2tnLt4smK07Rujr7Z4wr976l9m8txPlOSpJ0UY8aWR6nc+snTnoFUC2fuSeh37L6N59LDts/vjyeutfilbXZMXkfGbtzEGvTUxW8T9WP9Lb00VfbzePje7jp79+kNvu38kciSfNm8OhBz2Jv159wpTbWL1yMWs3bAaSK++du/cyPDLKOauWzihtZtXMNJ+ZNYPr9NpArYr/yvqR+d3z6Jo7l4d/+xhBADBHqms7M6m7caMEm66i1WlaufhOrw1kAxsw/n/s7ipbP3LX9kd58kFdPPb4PF504lMAGB4ZZd3GobpOKtOtu6mVNp/EbDJFq9O08nDQawO1Kv4r60d27d7LvDlwcHfXfus2O22dwEW3ZuXi4s02sGRhDzt3750wb6ziv7IPVddcsWvPPo49/KD91m122orORbdm5eOg1wZqdQ6urB856YiDWX7YQXTNnduUjs9F62g9He5PZlY+Lt5sA2OBLVvMds6qpePFbJX1I5VFctl1m522Iuv0olsz25+DXpuYTsV/sxsJdGqjBPcnMysfF29aaXVy0a2ZVec7vTblVoX56+SiWzOrzkGvDVWOwjLWqtAdfBuvU4tubfp8oVkOLt5sQ25VaNZc7r5SHg56bchPsjZrLl9oloeLN1tgqmIUtyo0ay53XykP3+k1WT3FKG5VaNZcnTzykE3koNdk9RSjeJR6s+byhWZ5uHizyeotRmlVq0K3YLMycveV8nDQa7J2rq9zVwkrM3dfKQcXbzZZOxejuAWbmXU6B70ma+f6OneVMLNO5+LNFmjXYpR2Lno1M2sE3+nZuHYuejUza4RS3+m5peJEbsFmZp2utEHPLRWra9eiVzOzRiht0Mu2VATG/6/bODTlSd93iO3N34+ZTaa0dXozbano0djbm78fM6ultEFvpmPtuS9be/P3Y2a1lDbozbSlovuytTd/P2ZWS2nr9GbaUrFRfdnqqXdy3dT0ua9h6zi/WhGU9k4PksD31jOO54OvfAZvPeP4un6gjejLVk+9k+umZsZ9DVvD+dWKotRBbyYaMYxYPfVOrpuamXYe5q2TOb9aUZS2eHM2ZtuXrZ7HC/lJzjPnvobN5/xqReE7vRaop+Won+RsReL8akXhoNcClfVOdz+wi5/++kFuuW+YS9ffwabB4batm9o0mKTxgi/dNJ5Ws3bNr2aVWhL0JN0t6ZeSbpQ0kM47VNJ6SXem/w9pRdqaIVvvdNv9j3D7/bs4/vD5rOg7eLwBANB2dVNurGCTcV2qFYUiovkble4G+iPigcy89wMPRcQlkt4BHBIRb6/1Of39/TEwMJBvYnN26fo79mtiP/b6rWcc38KU7a9IaQU3oTcrK0k3RER/tWXtVLx5NnBFOn0F8NLWJaV5itSZukhp9V2pmVXTqqAXwPck3SBpTTpvcUQMAqT/D6/2RklrJA1IGti+fXuTkpufIjUAKFJa3YTezKppVdB7bkScArwYOF/SafW+MSLWRkR/RPQvWrQovxQ2SZEaABQprUW6KzWz5mlJ0IuI+9L/24CvAacCQ5L6ANL/21qRtmYrUgOAIqW1SHelZtY8Te+cLukgYE5E7EynXwT8HXA1cB5wSfr/G81OW6sUqTN1UdK6euXi8VawC7rnsXP3XoZHRjln1dIWp8zMWqkVI7IsBr4maWz7/xIR6yT9HLhK0muBe4BXtiBt1iFmOqC4mXW2pge9iPg18Iwq8x8EXtjs9NjstHO3gKLclZpZ87RTlwUrGHcLMLOicdCzGXO3ADMrGgc9mzF3CzCzonHQsxlztwAzKxo/T28G2rnxRrNsGhzmgZ27+be7HuSQA7tY0beA7q557hZgZm3Nd3rT5MYbTxyDrnlzee6xhwLw4189xGN797VtZ3UzM/Cd3rRlG28A4//XbRwqzcl+4jHo4vATesaftlCWY2BmxeQ7vWly4w0fAzMrLge9aXLjDR8DMysuB71pKtKTBvLiY2BmReWgN01FetJAXnwMzKyo3JBlBjymo4+BmRWT7/TMzKw0HPTMzKw0HPTMzKw0HPTMzKw0HPTMzKw0HPTMzKw0HPTMzKw0HPTMzKw0HPTMzKw0HPTMzKw0HPTMzKw0HPTMzKw0FBGtTsOMSdoO/KbV6UgdBjzQ6kS0mI+BjwH4GICPAbT2GBwdEYuqLSh00GsnkgYior/V6WglHwMfA/AxAB8DaN9j4OJNMzMrDQc9MzMrDQe9xlnb6gS0AR8DHwPwMQAfA2jTY+A6PTMzKw3f6ZmZWWk46JmZWWk46M2ApLsl/VLSjZIG0nmHSlov6c70/yGtTmejSfq0pG2SNmbmTbrfki6UdJek2yWd2ZpUN9Ykx+AiSVvT/HCjpLMyyzrqGEg6UtIPJW2SdIukN6fzS5MPahyDMuWDbknXS7opPQbvSee3fz6ICP9N8w+4GzisYt77gXek0+8A3tfqdOaw36cBpwAbp9pv4ETgJuAAYDnwK2Buq/chp2NwEXBBlXU77hgAfcAp6fQC4I50P0uTD2ocgzLlAwHz0+ku4GfAs4uQD3yn1zhnA1ek01cAL21dUvIRERuAhypmT7bfZwNXRsSeiNgM3AWc2ox05mmSYzCZjjsGETEYEb9Ip3cCm4AllCgf1DgGk+nEYxARsSt92ZX+BQXIBw56MxPA9yTdIGlNOm9xRAxC8qMADm9Z6pprsv1eAmzJrHcvtU8MRfdGSTenxZ9jRTodfQwkLQOeSXKVX8p8UHEMoET5QNJcSTcC24D1EVGIfOCgNzPPjYhTgBcD50s6rdUJakOqMq9T+8d8DDgGOBkYBD6Uzu/YYyBpPvAV4C0R8UitVavM69RjUKp8EBH7IuJkYClwqqSVNVZvm2PgoDcDEXFf+n8b8DWS2/QhSX0A6f9trUthU0223/cCR2bWWwrc1+S0NUVEDKUngMeBy3ii2KYjj4GkLpKT/ecj4qvp7FLlg2rHoGz5YExE7ACuBVZTgHzgoDdNkg6StGBsGngRsBG4GjgvXe084ButSWHTTbbfVwPnSjpA0nLgOOD6FqQvd2M/8tTLSPIDdOAxkCTgU8CmiPhwZlFp8sFkx6Bk+WCRpIXpdA9wOnAbRcgHrW4FVLQ/4KkkrZBuAm4B3pXOfzJwDXBn+v/QVqc1h33/AkmxzSjJldtra+038C6SVlq3Ay9udfpzPAafBX4J3Ezy4+7r1GMA/B5JsdTNwI3p31llygc1jkGZ8sHTgf9I93Uj8Dfp/LbPBx6GzMzMSsPFm2ZmVhoOemZmVhoOemZmVhoOemZmVhoOemZmVhoOemY5k/TkzMj791eMxP+kFqXpWkn9rdi2WSvNa3UCzDpdRDxIMjQVki4CdkXEB8eWS5oXEXtbkzqzcvGdnlkLSLpc0ocl/RB4X/ostgsyyzemgxkj6dXps8tulPQJSXMrPuvFkq7KvH6+pH9Npz8maSD7zLMqadmVmX6FpMvT6UWSviLp5+nfcxt4CMxawkHPrHWOB06PiLdNtoKkFcA5JIOcnwzsA/60YrX1wLPTYfFI1/9iOv2uiOgnGUHjeZKePo30fQS4NCJWAX8MfHIa7zVrSy7eNGudL0XEvinWeSHwu8DPkyEf6aFiMPOI2CtpHfASSV8G/gD463Txn6SPv5pH8vDTE0mGjqrH6cCJ6XYBDpa0IJJnyJkVkoOeWes8mpney8SSl+70v4ArIuLCKT7ri8D5JA+4/XlE7EwH9r0AWBURD6fFlt1V3psdizC7fA7wnIgYmXJPzArCxZtm7eFu4BQASacAy9P51wCvkHR4uuxQSUdXef+16ftfxxNFmweTBNZhSYtJnv9YzZCkFZLmkDwdYMz3gDeOvZB08rT3yqzNOOiZtYevAIemT6L+b8AdABFxK/Bu4HuSbiapv+urfHNaTPpNksD2zXTeTSQj4d8CfBr490m2/Y70PT8geYLEmDcB/emTwG8F3jC7XTRrPT9lwczMSsN3emZmVhoOemZmVhoOemZmVhoOemZmVhoOemZmVhoOemZmVhoOemZmVhr/H7cgXei/OBz6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "Train score: 1.0\n",
      "Test score: 0.11251221803212363\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 4702 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age\n",
      "Train score: 1.0\n",
      "Test score: 0.1082113754108619\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 4725 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_columns = training_df.columns\n",
    "feat_columns = list(feat_columns)\n",
    "X_train, X_test, y_train, y_test = prepare_train_data(training_df, 'y')\n",
    "print(\"Complete\")\n",
    "complete = decision_tree_c(X_train,y_train, X_test, y_test)\n",
    "test_list = []\n",
    "for feat in feat_columns:\n",
    "    poped = feat_columns.pop()\n",
    "    if poped == 'y':\n",
    "        feat_columns.insert(0,poped)\n",
    "        continue\n",
    "    else:\n",
    "        print(poped)\n",
    "        training_df_2 = training_df[feat_columns]\n",
    "        X_train, X_test, y_train, y_test = prepare_train_data(training_df_2, 'y')\n",
    "        test = decision_tree_c(X_train,y_train,X_test, y_test)\n",
    "        test_list.append((poped,test))\n",
    "        feat_columns.insert(0,poped)\n",
    "        \n",
    "#feat_importance = pd.DataFrame(test_list)\n",
    "#feat_importance['importance'] = complete - feat_importance[1]\n",
    "#feat_importance.columns = [['feature', 'auPRC','importance']]\n",
    "#feat_importance.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "beginning-exhibit",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 1.0\n",
      "Test score: 0.1082113754108619\n",
      "Mean squared error: 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 4725 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decision_tree_c(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "danish-calendar",
   "metadata": {},
   "source": [
    "## How can we improve on the performance of Decision Trees and Reduce overfitting?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "emerging-domain",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_score_regressor(sklearn_regressor, X_train, y_train, X_test, y_test, model_parameters, print_oob_score=False):\n",
    "    \"\"\"A helper function that:\n",
    "        - Trains a regressor on training data\n",
    "        - Scores data on training and test data\n",
    "        - Returns a trained model\n",
    "    \"\"\"\n",
    "    # Step 1: Initializing the sklearn regressor \n",
    "    regressor = sklearn_regressor(**model_parameters)\n",
    "    \n",
    "    # Step 2: Training the algorithm using the X_train dataset of features and y_train, the associated target features\n",
    "    regressor.fit(X_train, y_train)\n",
    "    \n",
    "    # Step 3: Calculating the score of the predictive power on the training and testing dataset.\n",
    "    training_score = regressor.score(X_train, y_train)\n",
    "    testing_score = regressor.score(X_test, y_test)\n",
    "    \n",
    "    # Print the results!\n",
    "    print(f\"Train score: {training_score:>5.4f}\")\n",
    "    print(f\"Test score: {testing_score:>7.4f}\")\n",
    "    if print_oob_score:\n",
    "        print(f\"OOB score: {regressor.oob_score_:>8.4f}\")\n",
    "        \n",
    "    return regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "random-forge",
   "metadata": {},
   "source": [
    "### Hyper-paramter tunning\n",
    "\n",
    "Since the tree is overfitting, we need to use some of the strategies learnt in class, and aim to reduce them. To reduce the variance, we constrain the model using some of the provided parameters for example:\n",
    "- Criterion (Cost function used to measure the purity of a split)\n",
    "- Maximum depth of the tree\n",
    "- Minimum samples for each node split\n",
    "- Minimum samples for each terminal node\n",
    "- Maximum number of terminal nodes\n",
    "\n",
    "See this [useful blog](https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/#four) for a refresher on decision tree parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "successful-rubber",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.8618\n",
      "Test score:  0.1687\n"
     ]
    }
   ],
   "source": [
    "# Define the model parameters \n",
    "# We are fixing the random state so that the results are reproducible and consistent.\n",
    "hyper_parameters = {\"max_depth\":8,\n",
    "              'random_state':4,\n",
    "              'min_samples_split':3\n",
    "             }\n",
    "\n",
    "# Train and evaluate the model\n",
    "trained_regressor = train_score_regressor(sklearn_regressor=DecisionTreeRegressor,\n",
    "                                          X_train=X_train, \n",
    "                                          y_train=y_train, \n",
    "                                          X_test=X_test, \n",
    "                                          y_test=y_test, \n",
    "                                          model_parameters=hyper_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worldwide-memorial",
   "metadata": {},
   "source": [
    "## Using GridSearch to determine the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "engaging-bacteria",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=DecisionTreeRegressor(random_state=42),\n",
       "             param_grid={'max_depth': [4, 8, 10, 14],\n",
       "                         'min_impurity_decrease': [0.1, 0.01, 0.0],\n",
       "                         'min_samples_split': [10, 5, 50, 2, 20]})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set parameters to search through - known as parameter grid\n",
    "parameters = {'max_depth':[4, 8,10,14], \n",
    "              'min_impurity_decrease': [.1,.01, 0.0],\n",
    "              #'max_features': [3, 5,7],\n",
    "              'min_samples_split': [10, 5, 50, 2, 20]}\n",
    "# Initialize model\n",
    "decision_regressor= DecisionTreeRegressor(random_state=42)\n",
    "\n",
    "# Initialize GridSearch and then fit\n",
    "regressor = GridSearchCV(decision_regressor, parameters)\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "productive-manor",
   "metadata": {},
   "source": [
    "#### We can see that the search yielded some parameters we can use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "driving-climb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'mse',\n",
       " 'max_depth': 4,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.1,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 50,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'random_state': 42,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out what GridSearchCV found to be the best parameters \n",
    "regressor.best_estimator_.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civil-porcelain",
   "metadata": {},
   "source": [
    "#### Now let us use the parameters and see if we have reduced overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "flush-operator",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.5405\n",
      "Test score:  0.3483\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the tuned model\n",
    "trained_regressor = train_score_regressor(sklearn_regressor=DecisionTreeRegressor,\n",
    "                                          X_train=X_train, \n",
    "                                          y_train=y_train, \n",
    "                                          X_test=X_test, \n",
    "                                          y_test=y_test, \n",
    "                                          model_parameters=regressor.best_estimator_.get_params())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "magnetic-characterization",
   "metadata": {},
   "source": [
    "#### We can observe an improved test score, although the model's perfomance is still poor. \n",
    "\n",
    "Overfitting has been eliminated. Now we need to figure out otehr ways of improving the model accuracy. How about exploring a combination of parameter tunning and feature selection?\n",
    "\n",
    "#### Since we are using decision trees, we can easily extract information about the contributions of each feature to the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "colored-complaint",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VlnEG+aQkAhkAgCPSriXs85Ikp1fVNUnem+SWJD+b5O4k7+ruFy/hmAAAsBDLCOQXJzmzux9VVU9N8pIkj+nuO6rqlAPdoar2JtmbJLt3717CSAAAMJ1lv0jvyUne0N13JEl3f/lAG3X3vu7e6O6N0047bckjAQDAwS07kCtJL/kYAACwMMsI5NuT3Gty+T1Jnl9VxyfJwZ5iAQAAh4uFB3J3fynJVVV1Q5InJbksyebkRXsXL/p4AACwSMt4kV66+8ItV12yjOMAAMCi+SQ9AAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGMwUyFV1z6r606q6tqpuqKpnVtWjq+qKqrq6qt5dVfetqhOr6qaqOmNyvzdX1c8v9o8AAACLs2vG+z0lyee7+6eSpKpOTPKuJE/r7luq6plJXt7dz6+qi5JcWlWvTnJyd792686qam+SvUmye/fuGUcCAID5VXdv/05VD0ry7iR/mOSdSb6S5INJ/nqyyTFJvtDd/2yy/b4kz0jyyO7+3Pfa98bGRm9ubm57JgAA2I6qurq7N7ZeP9MZ5O7+ZFU9Osl5SX4jyXuTfLy7zz7Agb8vyUOSfCPJKUm+ZyADAMA6zfoc5B9Kckd3vynJK5M8JslpVXX25PZjq+phk81/Mcknkjw7yeur6tj5xwYAgOWY9TnID0/yiqq6O8l3krwgyZ1JfnvyfORdSX6rqr6T5N8kOau7b6+qK5P8lyQvm390AABYvFmfYvHu7H8O8lZPOMB1Dxnu9+9nOR4AAKyK90EGAICBQAYAgIFABgCAgUAGAICBQAYAgIFABgCAgUAGAICBQAYAgIFABgCAgUAGAICBQAYAgIFABgCAgUAGAICBQAYAgIFABgCAQXX3umf4/1TV7UluWvccR6FTk9y67iGOQtZ99az5elj39bDuq2fN12PWdf/h7j5t65W75p9n4W7q7o11D3G0qapN67561n31rPl6WPf1sO6rZ83XY9Hr7ikWAAAwEMgAADA4HAN537oHOEpZ9/Ww7qtnzdfDuq+HdV89a74eC133w+5FegAAsE6H4xlkAABYG4EMAACDtQVyVT2lqm6qqk9V1YsPcHtV1W9Pbr+uqn5sHXPuNFOs+4Or6kNV9a2qungdM+40U6z5cyaP8euq6oNV9ch1zLnTTLHuT5us+TVVtVlV56xjzp3mUOs+bPfjVXVXVV2wyvl2oike6+dW1W2Tx/o1VfXSdcy500zzWJ+s/TVV9fGqumLVM+5EUzzef2l4rN8w+T1zyrYP1N0r/0pyTJJPJ/nHSY5Lcm2Sh27Z5rwk70pSSR6b5CPrmHUnfU257vdJ8uNJXp7k4nXPfKR/Tbnmj0ty8uTyUz3WV7buJ+T/vQ7jEUluXPfcR/rXNOs+bPe+JH+W5IJ1z30kf035WD83yTvXPetO+ppy3U9K8r+T7J78fJ91z32kf037O2bY/vwk75vlWOs6g3xWkk91919397eTvCXJ07Zs87Qkv9/7fTjJSVV131UPusMcct27+4vd/dEk31nHgDvQNGv+we7+yuTHDye5/4pn3ImmWfev9eQ3aJJ7JvGK5flN87s9SV6U5I+SfHGVw+1Q0645izXNul+Y5G3d/bfJ/r9fVzzjTrTdx/uzk7x5lgOtK5Dvl+Szw8+fm1y33W3YHmu6ettd83+d/f/nhPlMte5V9fSqujHJnyZ5/opm28kOue5Vdb8kT0/yeyucayeb9nfM2VV1bVW9q6oetprRdrRp1v1BSU6uqsur6uqqeu7Kptu5pv47taqOT/KU7P/H+Lat66Om6wDXbT17M802bI81Xb2p17yqfjL7A9lzYec31bp399uTvL2qnpDk15I8edmD7XDTrPtvJflP3X1X1YE2Z5umWfOPJfnh7v5aVZ2X5I+TPHDZg+1w06z7riSPTvKkJD+Q5ENV9eHu/uSyh9vBttMx5ye5qru/PMuB1hXIn0vyj4af75/k8zNsw/ZY09Wbas2r6hFJXpfkqd39pRXNtpNt67He3VdW1elVdWp337r06XauadZ9I8lbJnF8apLzqurO7v7jlUy48xxyzbv7q8PlP6uq13isz23ajrm1u7+e5OtVdWWSRyYRyLPbzu/2Z2XGp1ck63uKxUeTPLCqHlBVx2X/H+KyLdtcluS5k3ezeGyS27r7C6sedIeZZt1ZrEOueVXtTvK2JD/rzMLCTLPuP1KTSpu8S85xSfzjZD6HXPfufkB37+nuPUnemuSF4ngu0zzWf3B4rJ+V/X/3e6zPZ5q/T9+R5PFVtWvyv/sfk+QTK55zp5mqY6rqxCRPzP7/BjNZyxnk7r6zqi5K8u7sf0Xi67v741X1C5Pbfy/7X918XpJPJbkjyc+tY9adZJp1r6ofTLKZ5B8kubuq/l32v0L0qwfbLwc35WP9pUnuneQ1k7/D7uzujXXNvBNMue7PyP5/hH8nyTeSPHN40R4zmHLdWaAp1/yCJC+oqjuz/7H+LI/1+Uyz7t39iar68yTXJbk7yeu6+4b1TX3k28bvmKcnec/k7P1MfNQ0AAAMfJIeAAAMBDIAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAM/i/UvGNNWPblcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the feature importances from our final trained model...\n",
    "importances = trained_regressor.feature_importances_\n",
    "\n",
    "# Find the indices of the feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Plotting a bar chart of feature importances in descending order\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.barplot(y=X_train.columns[indices],x=importances[indices]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sensitive-capability",
   "metadata": {},
   "source": [
    "## 3. Bagging\n",
    "<a id='bagging'></a>\n",
    "\n",
    "As Decision Trees naturally overfit to the data, better performance is achieved when an **ensemble approach** such as Bagging is used.\n",
    "\n",
    "Bagging is a shorter term for Bootstrapping Aggregation which is a general procedure to reduce variance in models that have a tendancy to overfit.\n",
    "\n",
    "[Bagging](https://www.ibm.com/cloud/learn/bagging)\n",
    "\n",
    "\n",
    "[Great tutorial on bagging and Boosting](https://towardsdatascience.com/ensemble-learning-bagging-boosting-3098079e5422)\n",
    "\n",
    "As an ensemble method, bagging does the following:\n",
    "- Creates N random subsamples of the dataset using selection with replacement. (Statistically 1/3 of the data is left of each subsample)\n",
    "- Trains a new decision tree on each N subsample\n",
    "- Takes the prediction from each and every N tree and averages the result\n",
    "- This average is the final output of the bagging model. \n",
    "\n",
    "This **Ensemble approach** uses the results of several random analyses to find an average response.\n",
    "\n",
    "For each tree, there will be a sample of data which was not used to train the algorithm. This means that we can also measure the **out-of-bag** score ( or **oob_score**). This score evaluates the performance of the model on the sample not used while training an individual tree. (On average, a random ~1/3 of data are not used to train a given tree)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "consecutive-shanghai",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.9389671361502347\n",
      "Test score: 0.9440559440559441\n"
     ]
    }
   ],
   "source": [
    "# For this basic implementation, we only need these modules\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "# Load the well-known Breast Cancer dataset\n",
    "# Split into train and test sets\n",
    "x, y = load_breast_cancer(return_X_y=True)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=23)\n",
    "\n",
    "# For simplicity, we are going to use as base estimator a Decision Tree with fixed parameters\n",
    "tree = DecisionTreeClassifier(max_depth=3, random_state=23)\n",
    "\n",
    "# The baggging ensemble classifier is initialized with:\n",
    "# base_estimator = DecisionTree\n",
    "# n_estimators = 5 : it's gonna be created 5 subsets to train 5 Decision Tree models\n",
    "# max_samples = 50 : it's gonna be taken randomly 50 items with replacement\n",
    "# bootstrap = True : means that the sampling is gonna be with replacement\n",
    "bagging = BaggingClassifier(base_estimator=tree, n_estimators=5, max_samples=50, bootstrap=True)\n",
    "\n",
    "# Training\n",
    "bagging.fit(x_train, y_train)\n",
    "\n",
    "# Evaluating\n",
    "print(f\"Train score: {bagging.score(x_train, y_train)}\")\n",
    "print(f\"Test score: {bagging.score(x_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "essential-secondary",
   "metadata": {},
   "source": [
    "## 4. Build a Random Forest\n",
    "<a id='randomforest'></a>\n",
    "\n",
    "Even with Bagging, the decision trees can be largely correlated. This is because given subsets from the same dataset, the trees are probably going to split on the same features and result in very similar predictions.\n",
    "\n",
    "To de-correlate the trees, we use a Random Forest Regressor, which at each split, only consider a random subsample of the features! This means that the trees are forced to make different split decisions and results in less correlated trees.\n",
    "\n",
    "**A Random Forest is essentially bagged decision trees where each decision tree only considers a random subset of the features at each split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "vertical-canon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features =  9\n"
     ]
    }
   ],
   "source": [
    "# Check how many columns our dataset has...\n",
    "print(f\"Number of features =  {X_train.shape[1]}\")\n",
    "num_features = X_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "following-transformation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.9223\n",
      "Test score:  0.4372\n",
      "OOB score:   0.4273\n"
     ]
    }
   ],
   "source": [
    "# define the parameters\n",
    "parameters = {\"oob_score\": True, \n",
    "              \"n_estimators\": 50, \n",
    "              \"max_features\": num_features, \n",
    "              'random_state': 42}\n",
    "\n",
    "trained_regressor = train_score_regressor(sklearn_regressor=RandomForestRegressor,\n",
    "                                          X_train=X_train, \n",
    "                                          y_train=y_train, \n",
    "                                          X_test=X_test, \n",
    "                                          y_test=y_test, \n",
    "                                          model_parameters=parameters,\n",
    "                                          print_oob_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "regular-banana",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot a graph of the true values vs the predicted values for the training and test datasets\n",
    "def plot_y_yhat_scatter(y_actual,y_predicted,train_test):\n",
    "    ax = sns.regplot(x=y_actual, y=y_predicted, fit_reg=False)   \n",
    "    ax.set_xlabel('true values')\n",
    "    ax.set_ylabel('predicted values')\n",
    "    ax.set_title('Relationship between true and predicted loan amounts: '+train_test+' results')\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "chronic-adoption",
   "metadata": {},
   "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": [
    "plot_y_yhat_scatter(y_train, trained_regressor.predict(X_train),train_test=\"training\")\n",
    "\n",
    "#In this kind of graphics could be nice to add a reference line binding all the perfect predictions points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "solar-wedding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uMW5UD+v7tnPJ7Uu5DBINdGkMvGlMU7tL9JmcpOnAy4E/hkkflrRE0rVh3C+IAmB8RNo1FAiKkuZIWiRp0YYNG5qZbOdcFa5esIKebjF6+DCk6H9Pt7h6wYrE0pQLvOv7tu8ReOcvX+9pypjEgpykMcCtwAVmtoVo5OfDiMZVWgd8JffVArMXGjF2rpn1mlnvxImZarDvXFtbvamfUT3de0wb1dPNmk39CaUonYE3jWnKgkSCXBi591bgejP7IYCZPWNmQ2EE3GuIiiQhyrnFh2afQjQYn3OuDUwdP5ptO/d8hL5t5xBTxo9OKEXpDLxpTFMWJFG7UkSj4y4zsytj0+MjQr+daFRriEZcPlPSCEmHAocTDRnvnGsD55w8g51DRv/AIGbR/51Dxjknz0gsTWkMvGlMUxYkkZN7NdEQ5q/Lay7wRUkPSVoC/A3wLwBmthSYB/wZ+AVwntesdK59zJo5ictOP5pJY0fy/LadTBo7kstOPzrRChVpDLxpTFMWZHI8ud7eXvMOmp1zpeRqMq7Z1M+UlNRkTDpNkhabWW/LfrAFPMg555wDshnkMjnUjnPOtRNvH9c83nelc84lyNvHNZcHOeecS5C3j2suD3LOOZcgbx/XXB7knHMuQd4+rrk8yDnnXIK8fVxzeZBzzrkEpbGxfJZ4EwLnnEvYrJmTPKg1iefknHPOZZYHOeecc5nlQc4551xm+TM551zb8W6wXKU8yDnnUqlYIMt1g9XTrT26wboMPNC5vXhxpXMudUr15+jdYLlqeJBzzqVOqUDm3WC5anhxpXOupSp5nrZ6Uz/jRvXsMS0XyKaOH836vu2MHv7i5cu7wXLFtDwnJ2mqpN9KWiZpqaTzw/QvSVouaYmk2ySNC9OnS9om6YHw9z+tTrNzrjEqHVamVH+O3g2Wq0YSxZWDwMfM7KXAicB5ko4C7gSOMbNjgb8AF8fmedzMjgt/57Y+yc65Rqj0eVqpQObdYLlqtLy40szWAevC6z5Jy4DJZnZH7GsLgXe2Om3OueYqVQwZN2vmJC4jCoprNvXvzsHlAlmrusHKL1p91Yz9uWfFRm+60EYSfSYnaTrwcuCPeR99ELg59v5QSfcDW4DPmNndBZY1B5gDMG3atKak1zlXn2qepyXdn2N+U4WVz27l3lUbmTR2OAfsM8KbLrSJxGpXShoD3ApcYGZbYtM/TVSkeX2YtA6YZmYvBz4K3CBp3/zlmdlcM+s1s96JEyc2fwWcc1Vrp+dp+UWrfdsH6RJs2TboTRfaSCJBTlIPUYC73sx+GJt+NvAW4CwzMwAz22Fmz4XXi4HHgSNan2rnXL3a6XlaflOFgaFddCn6n+NNF9Kv5cWVkgR8C1hmZlfGpp8KXAi81sz6Y9MnAhvNbEjSDOBwwG+dnGtTSRdDViq/aHV4dxcDQ7sY3v1i3sCbLqRfEjm5VwPvA14XaxZwGvA1YCxwZ15TgZOBJZIeBG4BzjWzjQmk2znXQfKLVseOHMYug31HDUt9Uat7kUKpYKb09vbaokWLkk6Gc67N5WpX5mp45mpXFqrxmQWSFptZb9LpaCTv8cQ554ooVLT6kYTS4mrjfVc655zLLA9yzjnnMsuDnHPOuczyIOeccy6zPMg555zLLA9yzjnnMsuDnHPOuczyIOeccy6zPMg555zLLO/xxLk65A+qmbVunpxrd56Tc65GuUE11/dtZ9yont2DaM5fvj7ppDnnAs/JOVej+KCaAKOHD6N/YJCrF6zw3FyH8Rx9enlOzrka5Q+qCT6IZifyHH26eU7Odbxa78LzB9UEH0SzE3mOPt08yLmOlrsL7+nWHnfhl0HZC9Q5J8/gktuX0j8wyKiebrbtHNprEM16i7G8GCz9Vm/qZ9yonj2meY4+PVpeXClpqqTfSlomaamk88P0/SXdKenR8H98bJ6LJT0m6RFJb2p1mjvJ/OXrmT13ISdd8Rtmz12Y+SKX+F24FP3v6RZXL1hRdt5ZMydx2elHM2nsSJ7ftpNJY0dy2elH7w5C9RZjeTFYe5g6fjTbdg7tMc1z9OmRRE5uEPiYmd0naSywWNKdwPuBX5vZ5ZIuAi4CLpR0FHAmcDRwMPArSUeY2VCR5bsa1ZOraVf13oUXGlQzp95irKwWg2Utd1pJjt4lp+VBzszWAevC6z5Jy4DJwBnArPC17wDzgQvD9JvMbAewUtJjwAnAPa1NefZl9aJaSjOfq9UbQLNYDFboRuoTtzzIAfsMZ+vAUFsGvVkzJ3EZ0fmzZlM/U9pwHbIs0WdykqYDLwf+CBwYAiBmtk5S7giZDCyMzbYmTHMNlsWLajnNvAuvN4BmsWJL/o3U0C5jU/9O+nYM8pKJY9q29KBUjt4lK7EmBJLGALcCF5jZllJfLTDNCixvjqRFkhZt2LChUcnsKJ34bKHcc7V6nHPyDHYOGf0Dg5hF/6sJoPXOn0b5zS429O2gS1Gwq/aZqHOVSCQnJ6mHKMBdb2Y/DJOfkXRQyMUdBOSerq8BpsZmnwI8lb9MM5sLzAXo7e3dKwi68jr12UKz7sLrLcbKYjFYfu50YGgXAoZ3v3i/nfXSA9daLQ9ykgR8C1hmZlfGProdOBu4PPz/cWz6DZKuJKp4cjhwb+tS3DmyeFFNWjUBtFiFjCxt/1fN2J//nv84g7t2MaK7CwG7DCaMGbH7O1kvPXCtlURO7tXA+4CHJD0Qpn2KKLjNk/Qh4EngXQBmtlTSPODPRDUzz/OalfUpVbstaxfVdtEJNVvnL1/PLfetZfzoHvq2D7JjcBcSjO7pYli3MLOOKT1wrSOz7JXs9fb22qJFi5JORirFL6bxIslGPYdytZk9d+FelUz6BwaZNHYkN845McGUNU6xdezpEuP3GeGlBykgabGZ9SadjkbyHk86TK3NBLLWtiltOqFma7F1fH7bTn7xL9kI5C59vIPmDlNLp8Le80bzdULN1k5YR5c+HuQ6TC0Xmnq6vnKVyWJzgXydsI4ufWoOcpJeLWmf8Pq9kq6UdEjjkuaaoZYLjQ8p03zNbK/XbJX2d9rO6+jaV80VTyQtAV4GHAt8j6hZwDvM7LWNS15tvOJJabnna5U+6O+EShGuNl6RKVu84smeBs3MJJ0BXGVm35J0dqMS5pqn2mYCndpI3JXXif2duvZST5Drk3QxUZu310jqBnrKzOPakDcSd8W0a61Qry3cOeoJcu8B/g74oJk9LWka8KXGJMuljTcSd4W0YyfSndDw3r2o5oonZvY0Uf+Tuf54ngVua0SiXKTTBjB17acda0x6beHOUk/tyn8AbgGuDpMmAz9qQJoc3jbNtYd2rDHptYU7Sz3FlecRDV76RwAzezQ2Bpyr0+U/X8b6vu0M7TKGd3cxYcyI3Xebab6AuM7TbkXZ7VjE6mpXT5DbYWYD0aACIGkYBcZ5c9Wbv3w9j27YSrdEt8TgkPHU89s4eL+RfrfpXJXyK5m8asb+3HLfWq8t3CHq6fHkLkmfAkZJegPwA+AnjUlWZ7t6wQp6urqQhCS6ukQX4pm+HX636VwVChX733LfWt55/OS2KmJ1tasnJ3cR8CHgIeAc4H+BbzYiUZ1u9aZ+Dtx3BOue38EuDAkMY3AIv9t0rgrF2vHds2Kjd2TQIWoOcma2C7gm/LkGyj0zOHjcSDb07WBgaBfdEodN3MfvNp2rQru246uFt/0rrOYgJ2klBZ7BmZlnNeqU62Gkp1scOmGf3c8MLjx1ZtJJawg/GV2rdEolE2/7V1w9z+R6gb8Of68B/gv4fiMS1enasVp2pbxphGuldmzHVwtv+1dcPcWVz+VN+k9JvwMuKTWfpGuBtwDrzeyYMO1m4MjwlXHAZjM7TtJ0YBnwSPhsoZmdW2ua20m7VcuulPd16FqpU7qk66Ri2WrVU1x5fOxtF1HObmwFs14HfA34bm6Cmb0nttyvAM/Hvv+4mR1XazpbyYvhymunk9H3ZzZk9YYxrlOKZWtRT+3Kr8ReDwKrgHeXm8nMFoQc2l4UNbp7N/C6OtKVCC8Tr0wrTsZGBCffn66d+EghxdVTXPk3jUxI8BrgGTN7NDbtUEn3A1uAz5jZ3U343bp5MVxlmn0yNio4NWJ/ZjknmLZ1S1t6Wq1TimVrUXWQk/TRUp+b2ZW1J4fZwI2x9+uAaWb2nKRXAD+SdLSZbSmQrjnAHIBp06bVkYTatFMxXJKafTI26maj3v2Z5Zxg2tYtbelJSicUy9ailpxcJc/dqha6BXsH8IrcNDPbAewIrxdLehw4Athr2G8zmwvMhWhk8GaksRQvE69cM0/GRt1s1Ls/s5yzT9u6pS09Ll2qDnJm9m/NSAhwCrDczNbkJkiaCGw0syFJM4DDgVTWifUy8XRo1M1Gvfszyzn7tK1b2tLj0qWeoXZGSjpP0tclXZv7q2C+G4F7gCMlrZH0ofDRmexZVAlwMrBE0oNEw/qca2Yba01zM2W5bVs7aVS7qHr359Txo9m2c2iPaUnm7Bs5NmHa1i1t6XHpIrPaSvYk/QBYTjQ6+GXAWcAyMzu/ccmrTW9vry1atFeJpmuBNFQAyKUhyQfw8edE8ZxgEjc+jU5LmtYtjelpZ5IWm1lv0ulopHqC3P1m9nJJS8zsWEk9wC/NLPHq/x7kkuEXmz2lIdgCzJ67cK8i3P6BQSaNHVlzJ8VpWbe0pqddZTHI1dNObmf4v1nSMcDTwPS6U+TallcA2FNaars145lVWtYtJ23pcelRT5CbK2k88K/A7cCY8Np1KK8AkE6NrvmbhiJp5ypVTwfN3zazTWZ2l5nNMLNJZnZ1w1Lm2o5XAEinRnZS7B1su3ZTT5BbKWmupNeH7rhch+uUHt/bTSNr/npv967d1FNceSTwVuA84FpJPwFuMrPfNSRlrmmaVdzkXQulV6OeWXmRtGs39fRduQ2YB8wLz+auAu4CuhuUNtcEze4CySsA7C1Lz7C8Zx/XbuoprkTSayV9HbgPGEkFoxC4xqmlga8XN7VW1p5heZG0azf19HiyErgAuBs4xszebWa3NiphrrRaL56rN/UzqmfPzLYXNzVP1m4qvGcf127qeSb3skKjAbjWqLVNmhc3tVYWn2F5kbRrJzXn5DzAJavWHJkXN7WWN6twLll1PZNzyan14unFTa3lNxXOJaue4kqXoHqGgvHiptbxZhXOJSttI4O7CjXi4pmlqu1p5jcVziWnnpHBjwT+mqjfSogahi9oRKJcZeq5eDa7vZxzzqVBzSODS7oDON7M+sL7S4EfNDR1HahVuav82plDu4z1W7ZzzvcXc/y08S3N1XmO0jnXLPVUPJkGDMTeD+BD7dSllQ2H47Uz+7bv5KnN29llxi6zljZYzlpj6bRq5MjgzrWTeoLc94B7JV0q6bPAH4HvlptJ0rWS1kt6ODbtUklrJT0Q/k6LfXaxpMckPSLpTXWkN/Va2XA4XjtzQ98OJBBieHdXSxssZ62xdBqDid9IuE5WTzu5zwMfADYBm4EPmNm/VzDrdcCpBab/h5kdF/7+F0DSUcCZwNFhnq9LymzfmK3sjSRetX3H4BBmxi6MCWNGNPV382WpB5ZGBpNGBsus3Ug4V41628mNBraY2VXAGkmHlpvBzBYAGytc/hlEIxvsMLOVwGPACTWnNuVa2XA43l6uu6uLri5x8H6j2Df0ztGqBstZaizdqGDS6JxXlm4knKtWPX1Xfha4ELg4TOoBvl9HWj4saUkozhwfpk0GVse+syZMy6RWNxyeNXMSN845kavf+womjR3JsG61vMFylhpLNyqYNDrnlaUbCeeqVU9O7u3A6cALAGb2FC82L6jWN4DDgOOAdcBXwvRCg7FaoQVImiNpkaRFGzZsqDEZyUqqN5Ike0HJUg8sjQomjc55ZelGwrlq1dPjyYCZmSQDkLRPrQsys2dyryVdA/w0vF0DTI19dQrwVJFlzAXmAvT29hYMhO0gqYbDSTZYzkpj6Xp6oYlrdCfa3uuK62T1BLl5kq4Gxkn6B+CDwDdrWZCkg8xsXXj7diBX8/J24AZJVwIHA4cD99aRZueaplHBpFHBMj9tHtRcJ6pnZPAvS3oDsIWo95NLzOzOcvNJuhGYBUyQtAb4LDBL0nFERZGrgHPCbyyVNA/4MzAInGdmQwUW61wqNCKYeM7LucaRWW0le5KuMLMLy01LQm9vry1atCjpZDjnXFuRtNjMepNORyPVU/HkDQWmvbmO5TnnikhjI3Pn2kEtoxD8I/BPwGGSlsQ+Ggv8oVEJc62XlT4k23E9SqXZO9N2rna15ORuIBpx4Mfhf+7vFWZ2VgPT5looK10/teN6lEuz91jiXO2qDnJm9ryZrQKuAjaa2RNm9gSwU9IrG51A1xpZuZC243qUS7P3WOJc7ep5JvcNYGvs/QthmmtDWbmQtuN6lEuz91jiXO3qCXKyWNVMM9tFfe3uXILSfCGtptJFmtejmHJp9h5LnKtdPUFuhaSPSOoJf+cD6S0TciWl9UJa7TO2tK5HKeXSnKWuz5xrtXrayU0C/gt4HVEj7l8DF5hZ4k/4vZ1cbXI1/NLUAHn23IV7dXHVPzDIpLEjuXHOiQXnSeN6lNOOaXbZk8V2cjUHuTTzIBdpx6r0+U664jeMG9WD9GJf3WbG89t2cveFr0swZZ0hC8eQq1wWg1wt7eQ+aWZflPRVCowIYGYfaUjKXF2y0raq0Z0Vu8pl5Rhyna2WZ3LLwv9FwOICfy4F2rEqfSHt+IwtK7JyDLnOVnVOzsx+Ev5/p/HJcY2yelM/48Io3zlpr0pfiHdWnJysHEOus9VSXPkTigxcCmBmp9eVItcQWSrm82FikpGlY8h1rlqKK79MNHL3SmAbcE3428qL48C5hHkxn6uXH0MuC2oprrwLQNL/b2Ynxz76iaQFDUuZq4sX87l6+THksqCeHkomSpphZisAJB0KTGxMslwjeDGfq5cfQ67d1RPk/gWYLylX1Wo6YURv55xzLg1qDnJm9gtJhwMzw6TlZraj3HySrgXeAqw3s2PCtC8RDdczADwOfMDMNkuaTtRk4ZEw+0IzO7fWNDuXBd5A27nK1dx3paTRwCeAD5vZg8A0SW+pYNbrgFPzpt0JHGNmxwJ/AS6Offa4mR0X/jzAuY7WjuPlOZekejpo/jZRzutV4f0a4HPlZjKzBcDGvGl3mNlgeLsQmFJHupzLLG+g7Vx16glyh5nZF4GdAGa2DVDpWSryQeDnsfeHSrpf0l2SXlNsJklzJC2StGjDhg0NSIZz6dOO4+U5l6R6gtyApFGEhuGSDgPKPpMrRdKngUHg+jBpHTDNzF4OfBS4QdK+heY1s7lm1mtmvRMneiVPl03tOF6ec0mqJ8h9FvgFMFXS9URD7Xyy1oVJOpuoQspZucFYzWyHmT0XXi8mqpRyRB1pdg1WzYCmrn7eQNu56tQU5CR1AeOBdwDvB24Ees1sfo3LOxW4EDjdzPpj0ydK6g6vZwCH4wOzpoZXgmg9H0DVuerU1ITAzHZJ+rCZzQN+Vs28km4EZgETJK0hyhFeDIwA7gzjhuWaCpwMXCZpEBgCzjWzjQUX3KbauTp4vBIEwOjhw+gfGOTqBSvaZh3akTfQdq5y9TQGv1PSx4GbgRdyE8sFITObXWDyt4p891bg1jrSmGrtPl6X91LvnEu7eoLcB8P/82LTDPCHAxVq95xQsV7qx4wYxuy5C9syd+qcy5Z6ejw5tJEJ6UTNzgk1uyj0nJNncMntS+kfGGRUTzfbdg6xZdtODBgY2tWWuVPnXLbU0+PJSEkflfRDSbdKukDSyEYmLqtyNRI39O3gsQ1b2bJt5+7PGlUdvBWVQgpVgjhgn+HsN6rHGys751KhnuLK7wJ9wFfD+9nA94B31ZuoLIs/h/urfUewdvN21m7eBhjDursaVh28VUWh+ZUgTrriN/6czjmXGvUEuSPN7GWx97+V9GC9Ccq6/OAjiaef387TW3Zw/LTxDStSzC8K3bJtJ89u3cGq5/qZPXdh056T+WjSzrk0qacx+P2STsy9kfRK4Pf1Jynb8rtlGjuyh5dMGsOksSO4cc6JDQs88Z4xtmzbyVPPb2NgaBcjutXU9mzeWNk5lyb1BLlXAn+QtErSKuAe4LWSHpK0pCGpy6BWdcsUDzbPbo16WxNi0r4jm/qczBsrO+fSpJ7iyvzhclwFCtVIbEZOZ9bMSVxGVDy66rl+RnRHAW7syKgIs5nPybyxsnMuLeppQvBEIxOSVo2uhh8PPms29TOlie3IcsFm9tyF/pzMOdeR6snJZV6zeiRpdU6nVblH55xLm3qeyWVeVgao9OdkzrlO5Tm5ErLUN2O53GM7dxTtnHPFeJArIS1tvpodgNq9o2jnnCvGiytLSEObr1Z0z9XOxbI+aKtzrhQPciWk4VlWKwJQfgN1aI9i2dwNwMpnt7LphQH+tGoj53x/Mf/1q78knTTnXEp4cWUZSbf5Wr2pn27Big1bGRjaxfDuLiaMGd7QAJSWYtlqXb1gBQODQzz3wgBdiGFdYsiM/57/OMdOGedFrc45z8ml3Zjh3azdvJ3BIaNbYnDIWLt5O/sM7y4/c4XSUCxbi9Wb+unbPkgXoqtLSKK7Swzu2tUWRa3OueZreZCTdK2k9ZIejk3bX9Kdkh4N/8fHPrtY0mOSHpH0planN2mSwovYX3x6AzSrWLbZz8umjh/NjsFdxDeFGYzo7kp9UatzrjWSKK68Dvga0VA9ORcBvzazyyVdFN5fKOko4EzgaOBg4FeSjjCzITpE345BJo8bybNbB3YXV/7VviPYumOwob/T6GLZVtTYPOfkGdz35CaGzOhWFODMYL99elJf1Oqca42W5+TMbAGwMW/yGcB3wuvvAG+LTb/JzHaY2UrgMeCEVqQzLaaOH82w7i5mTBzDzL/alxkTxzCsuyv1F/FWVJiZNXMS5806jC6JnUO76BYcMKaHnu7u1Be1OudaIy0VTw40s3UAZrZOUu5WfzKwMPa9NWHaXiTNAeYATJs2rYlJba00dclVTXu9VjWk/8gpR3DslHEt6QvUOdd+0hLkiin04MkKfdHM5gJzAXp7ewt+px21skPnUqotfmxljc2ka8A659IrLUHuGUkHhVzcQUCuhsIaYGrse1OAp1qeuoSl4SKeP6L56OHD6B8Y5OoFKwqmLU05UOdc50pLE4LbgbPD67OBH8emnylphKRDgcOBexNIX8ertsF4GhrSO+dcy3Nykm4EZgETJK0BPgtcDsyT9CHgSeBdAGa2VNI84M/AIHBeJ9WsTJNaih/TkAN1znW2lgc5M5td5KPXF/n+54HPNy9FrdWuvf178WNl2nX/OpdVaSmu7Ait6Gy5Wbz4sbx23r/OZVVaKp50hGorbyStUK7kxjknJp2s1Gq3/etcJ/Ag10LtNAhrp44xV09xYzvtX+c6hQe5Bip3gay08kYanut0Yq6k3sDerqM5OJdl/kwuqLcz4Uqex1TS239anutU02QgKwOX1tsVWbuO5uBclnmQo/bAEr+4f+Sm+9k5NFTyAllJ5Y20jNI9dfxotu3cs7VGsVxnGoJyI9Q7eKxXznEufby4ktqK5vKLtp5+fjvbBoYYMaybsSOj5zKFLpDl2o6l5blOpU0GslSs2YjiRm8b6Fy6eE6O2u7g83NcI4Z1gWBD347d36nleUylOahmqzRXUm/uJ028uNG57PGcHLXdwefnuCaMGcFTz29j++AQZlZzY+k0NbquJFeSpcoWaekM2znXOB7kqC2w5F/c9x3Vw47BIfoHhnh+286aL5DtdqFNU1BuBC9udC5bZJaZUWl26+3ttUWLFlU1T67afqWBJf5MLn5xb0ZFg0Y3KWjW8tohKDvnipO02Mx6k05HI3mQq0MrLu6NDqbllpeGNnpp49vEdQoPcm2iVUGuFWbPXbjXM6/+gUEmjR1ZUxdbpZaXK3psRe60XbQyx+5c0rIY5Lx2Zco1uvZiqeWlpY1emvg2ca69eZBLuUY3KSi1vCw1B2gU3ybOtTcPcinX6LZbpZaXljZ6aeLbxLn25kEu5RrdVVSp5Xlj6L35NnGuvaWm4omkI4GbY5NmAJcA44B/ADaE6Z8ys/8ttawsVTxpNW8OsDffJq5TZLHiSWqCXJykbmAt8ErgA8BWM/typfO3ugmBVy13zmVBFoNcWosrXw88bmZPJJ2QYrLU+75zzmVVWrv1OhO4Mfb+w5L+L7AI+JiZbcqfQdIcYA7AtGnTmp7ARvS+7znB8nwbOefqkbqcnKThwOnAD8KkbwCHAccB64CvFJrPzOaaWa+Z9U6cOLHp6ay3annac4JpGAg17dvIOZd+qQtywJuB+8zsGQAze8bMhsxsF3ANcEKiqQvqrVqe5kbGaQkuad5Gzrn2kMYgN5tYUaWkg2KfvR14uOUpKqDequVJNTKuJIeWluDiDbGdc/VKVZCTNBp4A/DD2OQvSnpI0hLgb4B/SSRxeeptv5ZEI+NKc2hpCS7eENs5V69UVTwxs37ggLxp70soOWXVM/ZYEuOwVVpZJi0DoWZtrDrnXOulKifXSWbNnMQ7j5/Mhr4dLHu6jw19O3jn8ZObWnOw0hxaWnr5aHRvL865zpOqnFwnmb98Pbfct5aJY0cwLeRSbrlvLcdOGde0i3ilObQ0jU7uI3U75+rhQS4hjWhnV61qiv88uDjnssCLKxOSROUOL/5zznUaz8klJKnKHZ5Dc851Es/JJSQtlTuccy7LPMglxIsOnXOu+by4MkFedOicc83lOTnnnHOZ5Tk55zLMhypync5zcs5lVFpGk3AuSR7knMuotIwm4VySPMg5l1FpGU3CuSR5kHMuo3yoIuc8yDmXWd7hgHMe5JzLLO9wwDlvQuBcpnmHA67TpSrISVoF9AFDwKCZ9UraH7gZmA6sAt5tZpuSSqNzzrn2kcbiyr8xs+PMrDe8vwj4tZkdDvw6vHfOOefKSmOQy3cG8J3w+jvA25JLinPOuXaStiBnwB2SFkuaE6YdaGbrAML/gg8YJM2RtEjSog0bNrQouc4559IsVc/kgFeb2VOSJgF3Slpe6YxmNheYC9Db22vNSqBzzrn2kaogZ2ZPhf/rJd0GnAA8I+kgM1sn6SCgbMd7ixcvflbSE01ObiETgGcT+N1WyfL6ZXndwNev3bVq/Q5pwW+0lMzSkemRtA/QZWZ94fWdwGXA64HnzOxySRcB+5vZJ5NMazGSFsUqzGROltcvy+sGvn7tLuvr10xpyskdCNwmCaJ03WBmv5D0J2CepA8BTwLvSjCNzjnn2khqgpyZrQBeVmD6c0S5Oeecc64qaatd2e7mJp2AJsvy+mV53cDXr91lff2aJjXP5JxzzrlG85ycc865zPIg55xzLrM8yNVI0ipJD0l6QNKiMG1/SXdKejT8H590Oisl6VpJ6yU9HJtWdH0kXSzpMUmPSHpTMqmuXJH1u1TS2rAPH5B0Wuyztlk/SVMl/VbSMklLJZ0fpmdi/5VYv6zsv5GS7pX0YFi/fwvTM7H/Emdm/lfDH9GICBPypn0RuCi8vgi4Iul0VrE+JwPHAw+XWx/gKOBBYARwKPA40J30OtSwfpcCHy/w3bZaP+Ag4Pjweizwl7AOmdh/JdYvK/tPwJjwugf4I3BiVvZf0n+ek2ustu1M2swWABvzJhdbnzOAm8xsh5mtBB4j6p0mtYqsXzFttX5mts7M7guv+4BlwGQysv9KrF8x7bZ+ZmZbw9ue8GdkZP8lzYNc7WruTLqNFFufycDq2PfWUPqik2YflrQkFGfmioPadv0kTQdeTpQbyNz+y1s/yMj+k9Qt6QGibgvvNLNM7r8keJCr3avN7HjgzcB5kk5OOkEtpALT2rEtyjeAw4DjgHXAV8L0tlw/SWOAW4ELzGxLqa8WmNaO65eZ/WdmQ2Z2HDAFOEHSMSW+3nbrlyQPcjWyWGfSwB6dSQNU2pl0yhVbnzXA1Nj3pgBPtThtdTOzZ8LFZRdwDS8W+bTd+knqIQoA15vZD8PkzOy/QuuXpf2XY2abgfnAqWRo/yXJg1wNJO0jaWzuNfBG4GHgduDs8LWzgR8nk8KGKbY+twNnShoh6VDgcODeBNJXl9wFJHg70T6ENls/RR2+fgtYZmZXxj7KxP4rtn4Z2n8TJY0Lr0cBpwDLycj+S1zSNV/a8Q+YQVS76UFgKfDpMP0A4NfAo+H//kmntYp1upGoyGcn0Z3ih0qtD/BpolpdjwBvTjr9Na7f94CHgCVEF46D2nH9gJOIiquWAA+Ev9Oysv9KrF9W9t+xwP1hPR4GLgnTM7H/kv7zbr2cc85llhdXOuecyywPcs455zLLg5xzzrnM8iDnnHMuszzIOeecyywPcq6jSRon6Z+STkeOpPdL+lrS6XAuKzzIuU43DigY5CR1tzYpzrlG8yDnOt3lwGFhPLIvSZoVxi67AXhI0vS8Meg+LunS8PowSb8InXTfLWlmfMGSuhSNOzguNu0xSQdKequkP0q6X9KvJB2YnzBJ10l6Z+z91tjrT0j6U+icODf+2D6SfhbGJXtY0nsat5mca0/Dkk6Acwm7CDjGos5xkTSLqA/EY8xsZej1vpi5wLlm9qikVwJfB16X+9DMdkn6MVGXU98O31llZs9I+h1wopmZpL8HPgl8rJIES3ojUVdOJxB11nt76CB8IvCUmf2f8L39KtwGzmWWBznn9navReN0FRV6xP//gB9EXSsC0SCW+W4GLgG+DZwZ3kPUqe7Nof/F4UDJ38vzxvB3f3g/hijo3Q18WdIVwE/N7O4qlulcJnmQc25vL8ReD7Jnsf7I8L8L2JzLAZZwD/ASSROJBr38XJj+VeBKM7s95B4vLTDv7t8OnRQPD9MFfMHMrs6fQdIriPp1/IKkO8zssjLpcy7T/Jmc63R9wNgSnz8DTJJ0gKQRwFsALBrPbKWkd0EUhCS9LH9mizqHvQ24kqgX/efCR/sBa8Prs/PnC1YBrwivzyAaMRrgl8AHQ24SSZMlTZJ0MNBvZt8HvgwcX3LNnesAnpNzHc3MnpP0+1C55OfAz/I+3ynpMqKRqFcSDYGScxbwDUmfIQpANxGNTJHvZuBPwPtj0y4lKupcCywEDi0w3zXAjyXdS9QL/QshTXdIeilwTygq3Qq8F3gJ8CVJu4hGW/jHCjeDc5nloxA455zLLC+udM45l1ke5JxzzmWWBznnnHOZ5UHOOedcZnmQc845l1ke5JxzzmWWBznnnHOZ9f8A10FceVoFkmgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_y_yhat_scatter(y_test, trained_regressor.predict(X_test),train_test=\"testing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "neither-buyer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=RandomForestRegressor(n_jobs=-1, random_state=42),\n",
       "             param_grid={'max_depth': [7, 10, 15],\n",
       "                         'n_estimators': [10, 50, 100, 200]})"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define parameters\n",
    "parameters = {'max_depth':[7,10,15], \n",
    "              'n_estimators': [10, 50,100,200]}\n",
    "# initialize model\n",
    "rf_regressor= RandomForestRegressor(n_jobs=-1, random_state=42)\n",
    "# initialize grid search and fit\n",
    "regressor = GridSearchCV(rf_regressor, parameters)\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "funded-relay",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.8465\n",
      "Test score:  0.4543\n"
     ]
    }
   ],
   "source": [
    "# Take the best parameters from grid search and fit a model\n",
    "parameters = regressor.best_estimator_.get_params()\n",
    "trained_regressor = train_score_regressor(sklearn_regressor=RandomForestRegressor,\n",
    "                                          X_train=X_train, \n",
    "                                          y_train=y_train, \n",
    "                                          X_test=X_test, \n",
    "                                          y_test=y_test, \n",
    "                                          model_parameters=parameters,\n",
    "                                          print_oob_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "logical-reviewer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0080305 , 0.38623132, 0.09287231, 0.05053225, 0.06201273,\n",
       "       0.04807416, 0.02369595, 0.25839651, 0.07015427])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trained_regressor.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "sharing-screening",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the feature importances from our final trained model...\n",
    "importances = trained_regressor.feature_importances_\n",
    "\n",
    "# Find the indices of the feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Plotting a bar chart of feature importances in descending order\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.barplot(y=X_train.columns[indices],\n",
    "            x=importances[indices]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "micro-henry",
   "metadata": {},
   "source": [
    "### How else can we improve on the model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "worldwide-pacific",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}