Scikit-learn LIBRARY.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eed62e47",
   "metadata": {},
   "source": [
    "## Scikit-learn LIBRARY FOR MACHINE LEARNING MODELING\n",
    "### AKA sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2974ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of contents\n",
    "workflow = [\n",
    "    \"An end-to-end Scikit-Learn workflow\",\n",
    "    \"Getting the data ready\",\n",
    "    \"Choose the right estimator/algorithm for our problems\",\n",
    "    \"Fit the model/algorithm and use it to make predictions on our data\",\n",
    "    \"Evaluating a model\",\n",
    "    \"Improve a model\",\n",
    "    \"Save and load a trained model\",\n",
    "    \"Putting it all together\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6c1304d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['An end-to-end Scikit-Learn workflow',\n",
       " 'Getting the data ready',\n",
       " 'Choose the right estimator/algorithm for our problems',\n",
       " 'Fit the model/algorithm and use it to make predictions on our data',\n",
       " 'Evaluating a model',\n",
       " 'Improve a model',\n",
       " 'Save and load a trained model',\n",
       " 'Putting it all together']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73fbdb42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# standart imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d1e427",
   "metadata": {},
   "source": [
    "### Getting our data ready to be used with machine learning\n",
    "\n",
    "Logics:\n",
    "* Split the data into features and labels \"usually 'X' and 'y'\".\n",
    "* Filling \"also called imputing\" or disregarding missing values.\n",
    "* Converting non-numerical values to numerical values \"also called feature encoding\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edba7846",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease = pd.read_csv('data/heart-disease.csv')\n",
    "heart_disease.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "917e260e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
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       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
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       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>2</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axis=1 is column\n",
    "# axis=0 is row\n",
    "\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4107147d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = heart_disease[\"target\"]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eed2b336",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0babac98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((242, 13), (61, 13), (242,), (61,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "631bd152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b338accb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242.4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape[0] * 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d59194dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(heart_disease)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5237dc",
   "metadata": {},
   "source": [
    "### Convert Data To Numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f6b3b4a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431</td>\n",
       "      <td>4</td>\n",
       "      <td>15323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714</td>\n",
       "      <td>5</td>\n",
       "      <td>19943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714</td>\n",
       "      <td>4</td>\n",
       "      <td>28343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365</td>\n",
       "      <td>4</td>\n",
       "      <td>13434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577</td>\n",
       "      <td>3</td>\n",
       "      <td>14043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price\n",
       "0   Honda  White          35431      4  15323\n",
       "1     BMW   Blue         192714      5  19943\n",
       "2   Honda  White          84714      4  28343\n",
       "3  Toyota  White         154365      4  13434\n",
       "4  Nissan   Blue         181577      3  14043"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales = pd.read_csv(\"data/car-sales-extended.csv\")\n",
    "car_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0672ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dac7f553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             object\n",
       "Colour           object\n",
       "Odometer (KM)     int64\n",
       "Doors             int64\n",
       "Price             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77b38eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into X/y\n",
    "X = car_sales.drop(\"Price\", axis=1)\n",
    "y = car_sales[\"Price\"]\n",
    "\n",
    "# Split into training and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "73a8a826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 3.54310e+04],\n",
       "       [1.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        1.00000e+00, 1.92714e+05],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 8.47140e+04],\n",
       "       ...,\n",
       "       [0.00000e+00, 0.00000e+00, 1.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 6.66040e+04],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 2.15883e+05],\n",
       "       [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 2.48360e+05]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn the categories into numbers\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features = [\"Make\", \"Colour\", \"Doors\"]\n",
    "one_hot = OneHotEncoder()\n",
    "transformer = ColumnTransformer([(\"one_hot\", one_hot, categorical_features)], remainder=\"passthrough\")\n",
    "\n",
    "transformed_X = transformer.fit_transform(X)\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc664205",
   "metadata": {},
   "outputs": [
    {
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       "     Make Colour  Odometer (KM)  Doors\n",
       "0   Honda  White          35431      4\n",
       "1     BMW   Blue         192714      5\n",
       "2   Honda  White          84714      4\n",
       "3  Toyota  White         154365      4\n",
       "4  Nissan   Blue         181577      3"
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   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "589aecbe",
   "metadata": {},
   "outputs": [
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       "      0    1    2    3    4    5    6    7    8    9    10   11        12\n",
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       "\n",
       "[1000 rows x 13 columns]"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(transformed_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "53171efd",
   "metadata": {},
   "outputs": [
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       "     Doors  Make_BMW  Make_Honda  Make_Nissan  Make_Toyota  Colour_Black  \\\n",
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       "\n",
       "     Colour_Blue  Colour_Green  Colour_Red  Colour_White  \n",
       "0              0             0           0             1  \n",
       "1              1             0           0             0  \n",
       "2              0             0           0             1  \n",
       "3              0             0           0             1  \n",
       "4              1             0           0             0  \n",
       "..           ...           ...         ...           ...  \n",
       "995            0             0           0             0  \n",
       "996            0             0           0             1  \n",
       "997            1             0           0             0  \n",
       "998            0             0           0             1  \n",
       "999            1             0           0             0  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(car_sales[[\"Make\", \"Colour\", \"Doors\"]])\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5f73979d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# refit the model\n",
    "np.random.seed(42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(transformed_X, y, test_size=0.2)\n",
    "\n",
    "# model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "932153ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb0de00b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3235867221569877"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build machine learning model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128901f6",
   "metadata": {},
   "source": [
    "### Handling Missing Values With Pandas\n",
    "\n",
    "* Fill them with some value \"also known as imputation\".\n",
    "* Remove the samples with missing data altogether."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ac4690e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0   Honda  White        35431.0    4.0  15323.0\n",
       "1     BMW   Blue       192714.0    5.0  19943.0\n",
       "2   Honda  White        84714.0    4.0  28343.0\n",
       "3  Toyota  White       154365.0    4.0  13434.0\n",
       "4  Nissan   Blue       181577.0    3.0  14043.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing = pd.read_csv(\"data/car-sales-extended-missing-data.csv\")\n",
    "car_sales_missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6fb45003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             49\n",
       "Colour           50\n",
       "Odometer (KM)    50\n",
       "Doors            50\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show how many missing values\n",
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2ca599a",
   "metadata": {},
   "source": [
    "### Option 1: Fill missing data with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a91ca682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0    811\n",
       "5.0     75\n",
       "3.0     64\n",
       "Name: Doors, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing[\"Doors\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "220c27eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill the \"Make\" column\n",
    "car_sales_missing[\"Make\"].fillna(\"missing\", inplace=True)\n",
    "\n",
    "# fill the \"Colour\" column\n",
    "car_sales_missing[\"Colour\"].fillna(\"missing\", inplace=True)\n",
    "\n",
    "# fill the \"Odometer (KM)\" column\n",
    "car_sales_missing[\"Odometer (KM)\"].fillna(car_sales_missing[\"Odometer (KM)\"].mean(), inplace=True)\n",
    "\n",
    "# fill the \"Doors\" column\n",
    "car_sales_missing[\"Doors\"].fillna(4, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "afcf3c7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make              0\n",
       "Colour            0\n",
       "Odometer (KM)     0\n",
       "Doors             0\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check our dataframe again\n",
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "69dafe94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove rows with missing Price value\n",
    "car_sales_missing.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bf994993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             0\n",
       "Colour           0\n",
       "Odometer (KM)    0\n",
       "Doors            0\n",
       "Price            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8ea8c368",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get data ready\n",
    "X = car_sales_missing.drop(\"Price\", axis=1)\n",
    "y = car_sales_missing[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e7d00beb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        3.54310e+04, 1.53230e+04],\n",
       "       [1.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        1.92714e+05, 1.99430e+04],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        8.47140e+04, 2.83430e+04],\n",
       "       ...,\n",
       "       [0.00000e+00, 0.00000e+00, 1.00000e+00, ..., 0.00000e+00,\n",
       "        6.66040e+04, 3.15700e+04],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        2.15883e+05, 4.00100e+03],\n",
       "       [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        2.48360e+05, 1.27320e+04]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn the categories into numbers\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features = [\"Make\", \"Colour\", \"Doors\"]\n",
    "one_hot = OneHotEncoder()\n",
    "transformer = ColumnTransformer([(\"one_hot\", one_hot, categorical_features)], remainder=\"passthrough\")\n",
    "\n",
    "transformed_X = transformer.fit_transform(car_sales_missing)\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d9905d",
   "metadata": {},
   "source": [
    "### Handling Missing Values With Scikit-learn\n",
    "#### Option 2: Fill missing values with sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ad37bb38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             49\n",
       "Colour           50\n",
       "Odometer (KM)    50\n",
       "Doors            50\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing = pd.read_csv(\"data/car-sales-extended-missing-data.csv\")\n",
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2743339c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             47\n",
       "Colour           46\n",
       "Odometer (KM)    48\n",
       "Doors            47\n",
       "Price             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the rows with no prices\n",
    "car_sales_missing.dropna(subset=[\"Price\"], inplace=True)\n",
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f2bb975f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into X and y\n",
    "X = car_sales_missing.drop(\"Price\", axis=1)\n",
    "y = car_sales_missing[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "26d9a1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Honda', 'White', 4.0, 35431.0],\n",
       "       ['BMW', 'Blue', 5.0, 192714.0],\n",
       "       ['Honda', 'White', 4.0, 84714.0],\n",
       "       ...,\n",
       "       ['Nissan', 'Blue', 4.0, 66604.0],\n",
       "       ['Honda', 'White', 4.0, 215883.0],\n",
       "       ['Toyota', 'Blue', 4.0, 248360.0]], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill missing values with Scikit-Learn\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# fill categorical values with 'missing' and numerical values with mean()\n",
    "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"missing\")\n",
    "door_imputer = SimpleImputer(strategy=\"constant\", fill_value=4)\n",
    "num_imputer = SimpleImputer(strategy=\"mean\")\n",
    "\n",
    "# define columns\n",
    "cat_features = [\"Make\", \"Colour\"]\n",
    "door_feature = [\"Doors\"]\n",
    "num_feature =[\"Odometer (KM)\"]\n",
    "\n",
    "# create an imputer \"something that fills missing data\"\n",
    "imputer = ColumnTransformer([\n",
    "    (\"cat_imputer\", cat_imputer, cat_features),\n",
    "    (\"door_imputer\", door_imputer, door_feature),\n",
    "    (\"num_imputer\", num_imputer, num_feature)\n",
    "])\n",
    "\n",
    "# Transform the data\n",
    "filled_X = imputer.fit_transform(X)\n",
    "filled_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0dd8715e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>5.0</td>\n",
       "      <td>192714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>84714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>154365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>3.0</td>\n",
       "      <td>181577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Black</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35820.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>missing</td>\n",
       "      <td>White</td>\n",
       "      <td>3.0</td>\n",
       "      <td>155144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>215883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>4.0</td>\n",
       "      <td>248360.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>950 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1    2         3\n",
       "0      Honda  White  4.0   35431.0\n",
       "1        BMW   Blue  5.0  192714.0\n",
       "2      Honda  White  4.0   84714.0\n",
       "3     Toyota  White  4.0  154365.0\n",
       "4     Nissan   Blue  3.0  181577.0\n",
       "..       ...    ...  ...       ...\n",
       "945   Toyota  Black  4.0   35820.0\n",
       "946  missing  White  3.0  155144.0\n",
       "947   Nissan   Blue  4.0   66604.0\n",
       "948    Honda  White  4.0  215883.0\n",
       "949   Toyota   Blue  4.0  248360.0\n",
       "\n",
       "[950 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(filled_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3fc80dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>5.0</td>\n",
       "      <td>192714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>84714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>154365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>3.0</td>\n",
       "      <td>181577.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour Doors Odometer (KM)\n",
       "0   Honda  White   4.0       35431.0\n",
       "1     BMW   Blue   5.0      192714.0\n",
       "2   Honda  White   4.0       84714.0\n",
       "3  Toyota  White   4.0      154365.0\n",
       "4  Nissan   Blue   3.0      181577.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_filled = pd.DataFrame(filled_X, columns=[\"Make\", \"Colour\", \"Doors\", \"Odometer (KM)\"])\n",
    "car_sales_filled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "10032566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             0\n",
       "Colour           0\n",
       "Doors            0\n",
       "Odometer (KM)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_filled.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "57530458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<950x15 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 3800 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn the categories into numbers\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features = [\"Make\", \"Colour\", \"Doors\"]\n",
    "one_hot = OneHotEncoder()\n",
    "transformer = ColumnTransformer([(\"one_hot\", one_hot, categorical_features)], remainder=\"passthrough\")\n",
    "\n",
    "transformed_X = transformer.fit_transform(car_sales_filled)\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "35e96197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21990196728583944"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data as number and filled \"no missing values\"\n",
    "# fit a model\n",
    "np.random.seed(42)\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(transformed_X, y, test_size=0.2)\n",
    "\n",
    "model = RandomForestRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5fc2b198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['An end-to-end Scikit-Learn workflow',\n",
       " 'Getting the data ready',\n",
       " 'Choose the right estimator/algorithm for our problems',\n",
       " 'Fit the model/algorithm and use it to make predictions on our data',\n",
       " 'Evaluating a model',\n",
       " 'Improve a model',\n",
       " 'Save and load a trained model',\n",
       " 'Putting it all together']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bba8e5a",
   "metadata": {},
   "source": [
    "## Choose the right estimator/algorithm for our problems\n",
    "\n",
    "Sklearn uses estimator as another term for machine learning model or algorithm.\n",
    "\n",
    "### Choosing The Right Model For Your Data\n",
    "\n",
    "* Classification: predicting whether a sample is one thing or another \"ex: heart disease\"\n",
    "* Regression: predicting a number \"ex: car sales price\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41cf350b",
   "metadata": {},
   "source": [
    "![sklearn_mi_map_cheat](documents/sklearn_mi_map_cheat.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27fd61ae",
   "metadata": {},
   "source": [
    "### Picking a machine learning model for a regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "af3d6840",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Boston housing dataset from https://www.kaggle.com/code/prasadperera/the-boston-housing-dataset\n",
    "# boston = pd.read_csv('data/boston_house_prices.csv', header=None, delimiter=r\"\\s+\", names=column_names)\n",
    "column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']\n",
    "\n",
    "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
    "raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
    "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
    "target = raw_df.values[1::2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "be61b07c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "boston_df = pd.DataFrame(data, columns=column_names)\n",
    "boston_df[\"target\"] = target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bee777bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  target  \n",
       "0     15.3  396.90   4.98    24.0  \n",
       "1     17.8  396.90   9.14    21.6  \n",
       "2     17.8  392.83   4.03    34.7  \n",
       "3     18.7  394.63   2.94    33.4  \n",
       "4     18.7  396.90   5.33    36.2  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "38c212f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "506"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many samples\n",
    "len(boston_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "00cf56db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6662221670168518"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ridge regression\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# create the data\n",
    "X = boston_df.drop(\"target\", axis=1)\n",
    "y = boston_df[\"target\"]\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Instantiate Ridge model\n",
    "model = Ridge()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# check the score of the Ridge model on test data\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0d707a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how do we improve this score?\n",
    "# what if Ridge wasn't working?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8d6ce8",
   "metadata": {},
   "source": [
    "### Choosing The Right Model For Your Data 2 (Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0d02f609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8654448653350507"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Regressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# create the data\n",
    "X = boston_df.drop(\"target\", axis=1)\n",
    "y= boston_df[\"target\"]\n",
    "\n",
    "# split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# instantiate Random Forest Regressor\n",
    "rf = RandomForestRegressor(n_estimators=100)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# evaluate the Random Forest Regressor\n",
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9245b4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6662221670168518"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the Ridge model again\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c74460f",
   "metadata": {},
   "source": [
    "### Choosing The Right Model For Your Data 3 (Classification)\n",
    "\n",
    "https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a094ec73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease = pd.read_csv(\"data/heart-disease.csv\")\n",
    "len(heart_disease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ad3146b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model \"LinearSVC\"\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# make the data\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "# split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# instantiate LinearSVC\n",
    "clf = LinearSVC(dual=False)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# evaluate the LinearSVC\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "976ad92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "111f03a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 61 points : 8\n"
     ]
    }
   ],
   "source": [
    "# predict\n",
    "y_pred = clf.fit(X_train, y_train).predict(X_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\"% (X_test.shape[0], (y_test != y_pred).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5b1a566d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model \"RandomForestClassifier\"\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# make the data\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "# split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# instantiate RandomForestClassifier\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "# evaluate the RandomForestClassifier\n",
    "rfc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b06ae42c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 61 points : 8\n"
     ]
    }
   ],
   "source": [
    "# predict\n",
    "y_predrfc = rfc.fit(X_train, y_train).predict(X_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\"% (X_test.shape[0], (y_test != y_predrfc).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025484be",
   "metadata": {},
   "source": [
    "#### Tidbit:\n",
    "* If you have structured data, use ensemble methods \"data of table\"\n",
    "* If you have unstructed data, use deep learning or transfer learning \"pictures, images, videos\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bac9f6",
   "metadata": {},
   "source": [
    "## Fit the model/algorithm and use it to make predictions on our data\n",
    "### Fitting A Model To The Data\n",
    "\n",
    "#### Different names for:\n",
    "* X = features, features variables, data\n",
    "* y = labels, targets, target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4fc26945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model \"RandomForestClassifier\"\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# make the data\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "# split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# instantiate RandomForestClassifier\n",
    "rfc = RandomForestClassifier()\n",
    "\n",
    "# fit the model to the data\n",
    "# training the machine learning model\n",
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "# evaluate the RandomForestClassifier\n",
    "# use the patterns the model has learned\n",
    "rfc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "59f5fd34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ef1ed2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb81bc57",
   "metadata": {},
   "source": [
    "## Making Predictions With Our Model\n",
    "\n",
    "##### two ways to make predictions\n",
    "* predict()\n",
    "* predict_proba()\n",
    "\n",
    "### Make predictions with `predict()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "916f11c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict target\n",
    "clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "21238c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# existing truth data\n",
    "np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "446d47a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare predictions to truth labels to evaluate the model\n",
    "y_preds = clf.predict(X_test)\n",
    "np.mean(y_preds == y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "025a572c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f764e728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23c6866",
   "metadata": {},
   "source": [
    "### Make predictions with `predict_proba()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "30d81c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89, 0.11],\n",
       "       [0.49, 0.51],\n",
       "       [0.43, 0.57],\n",
       "       [0.84, 0.16],\n",
       "       [0.18, 0.82]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict_proba() returns probabilities of a classification label\n",
    "# probability of [0, 1]\n",
    "# first example, \"0\" -> 0.89 probability, \"1\" -> 0.11 probability\n",
    "# so It is \"0\"\n",
    "rfc.predict_proba(X_test[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ba6158bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict()\n",
    "rfc.predict(X_test[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c5c782",
   "metadata": {},
   "source": [
    "#### when use predict_proba()\n",
    "#### that if you want to set probability % more than %80\n",
    "#### if that is the case, you can do with predict_proba()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186bd08d",
   "metadata": {},
   "source": [
    "### Making Predictions With Our Model (Regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c5c868",
   "metadata": {},
   "source": [
    "`predict()` can also be used for regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "87bce4e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# create the data\n",
    "X = boston_df.drop(\"target\", axis=1)\n",
    "y = boston_df[\"target\"]\n",
    "\n",
    "# split into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Instantiate and fit model\n",
    "model = RandomForestRegressor().fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_preds = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "719e55ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8654448653350507"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5ff26f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.081, 30.574, 16.759, 23.46 , 16.893, 21.644, 19.113, 15.334,\n",
       "       21.14 , 20.639])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "27d738d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.6, 32.4, 13.6, 22.8, 16.1, 20. , 17.8, 14. , 19.6, 16.8])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(y_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "79ed8e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.136382352941176"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare the predictions to the truth\n",
    "# meaning: as average there is a \"2.13\" diffrences \n",
    "# between prediction and truth data\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "mean_absolute_error(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db9cc46",
   "metadata": {},
   "source": [
    "## Evaluating a model\n",
    "\n",
    "### Evaluating A Machine Learning Model (Score)\n",
    "\n",
    "Three ways to evaluating modeling:\n",
    "* Estimator `score` method\n",
    "* The `scoring` parameter\n",
    "* Problem-specific metric functions\n",
    "\n",
    "#### Estimator Score Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b090802f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for classification\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X = heart_disease.drop('target', axis=1)\n",
    "y = heart_disease['target']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4a8ad3f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4e17dbf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e14339da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# create the data\n",
    "X = boston_df.drop(\"target\", axis=1)\n",
    "y = boston_df[\"target\"]\n",
    "\n",
    "# split into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Instantiate and fit model\n",
    "model = RandomForestRegressor().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4bb90a21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8654448653350507"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ad8efc",
   "metadata": {},
   "source": [
    "#### `.score` method is default calculation to find prediction percentage !!!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6220ec",
   "metadata": {},
   "source": [
    "### Evaluating A Machine Learning Model 2 (Cross Validation)\n",
    "\n",
    "#### The `scoring` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9f7cd189",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# for classification\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X = heart_disease.drop('target', axis=1)\n",
    "y = heart_disease['target']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "469dfde8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d4675f15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.86885246, 0.81967213, 0.78333333, 0.76666667])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cv = cross-validation numbers\n",
    "# test data can be splitted by cv, and train in as per this\n",
    "cross_val_score(clf, X, y, cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e09dc9",
   "metadata": {},
   "source": [
    "![crossvalidation](documents/cross_validation.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1531d66",
   "metadata": {},
   "source": [
    "![crossvalidation_lrn](documents/sklearn-cross-validation.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "63526df4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8524590163934426, 0.8248087431693989)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take average of cv learning %\n",
    "np.random.seed(42)\n",
    "\n",
    "# single training and test split score\n",
    "clf_single_score = clf.score(X_test, y_test)\n",
    "\n",
    "# take the mean of 5-fold cross-validation score\n",
    "clf_cross_val_score = np.mean(cross_val_score(clf, X, y, cv=5))\n",
    "\n",
    "# compare the two\n",
    "clf_single_score, clf_cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8968752f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# real learning score is: clf_cross_val_score\n",
    "# 0.8248087431693989"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9f9ee088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# default scorng parameter of classifier = mean accuracy\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ab3ed278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.83606557, 0.78333333, 0.78333333])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scoring parameter set to none by default meaning is\n",
    "# It use clf.score value in below\n",
    "np.random.seed(42)\n",
    "cross_val_score(clf, X, y, cv=5, scoring=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0847750c",
   "metadata": {},
   "source": [
    "### Evaluating A Classification Model 1 (Accuracy)\n",
    "\n",
    "#### Classification model evaluation metrics\n",
    "* Accuracy\n",
    "* Area under ROC curve\n",
    "* Confusion matrix\n",
    "* Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bf9e7fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b72b7a",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "016bcb37",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "cross_val_score = cross_val_score(clf, X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8d7086f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8248087431693989"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bd385aac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heart Disease Classifie Cross-Validated Accuracy: 82.48%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Heart Disease Classifie Cross-Validated Accuracy: {np.mean(cross_val_score) * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef0bf3a",
   "metadata": {},
   "source": [
    "## Evaluating A Classification Model 2 (ROC Curve)\n",
    "### Area under ROC curve\n",
    "\n",
    "**Area under the receiver operating characteristic curve (AUC/ROC)**\n",
    "\n",
    "* Area under curve (AUC)\n",
    "* ROC curve\n",
    "\n",
    "ROC curves are comparison of a model's true positive rate (tpr) versus a models false postive rate (fpr)\n",
    "\n",
    "* True positive = model predicts 1 when truth is 1\n",
    "* False positive = model predicts 1 when truth is 0\n",
    "* True negative = model predicts 0 when truth is 0\n",
    "* False negative = model predicts 0 when truth is 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6a5fdead",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c8de86d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.51, 0.49],\n",
       "        [0.17, 0.83],\n",
       "        [0.51, 0.49],\n",
       "        [0.72, 0.28],\n",
       "        [0.43, 0.57],\n",
       "        [0.12, 0.88],\n",
       "        [0.3 , 0.7 ],\n",
       "        [0.97, 0.03],\n",
       "        [0.15, 0.85],\n",
       "        [0.4 , 0.6 ]]),\n",
       " 61)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# fit the classifier\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# make predictions with probabilities\n",
    "y_probs = clf.predict_proba(X_test)\n",
    "\n",
    "y_probs[:10], len(y_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2e3a7ad8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.49, 0.83, 0.49, 0.28, 0.57, 0.88, 0.7 , 0.03, 0.85, 0.6 ])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_probs_positive = y_probs[:, 1]\n",
    "y_probs_positive[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "44bfbb4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.03448276, 0.03448276, 0.03448276, 0.03448276,\n",
       "       0.03448276, 0.03448276, 0.06896552, 0.06896552, 0.06896552,\n",
       "       0.10344828, 0.10344828, 0.13793103, 0.13793103, 0.13793103,\n",
       "       0.20689655, 0.20689655, 0.20689655, 0.27586207, 0.37931034,\n",
       "       0.37931034, 0.48275862, 0.48275862, 0.55172414, 0.55172414,\n",
       "       1.        ])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate fpr, tpr and thresholds\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_probs_positive)\n",
    "\n",
    "# check the false positive rates\n",
    "fpr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f0ea03",
   "metadata": {},
   "source": [
    "## Evaluating A Classification Model 3 (ROC Curve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e30d7771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a function for plotting ROC curves\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_roc_curve(fpr, tpr):\n",
    "    \"\"\"\n",
    "    Plots a ROC curve given the false positive rate (fpr)\n",
    "    and true positive rate (tpr) of a model.\n",
    "    \"\"\"\n",
    "    # plot roc curve\n",
    "    plt.plot(fpr, tpr, color=\"orange\", label=\"ROC\")\n",
    "    # plot line with no predictive power (baseline)\n",
    "    plt.plot([0, 1], [0, 1], color=\"darkblue\", linestyle=\"--\", label=\"Guessing\")\n",
    "    \n",
    "    # customize the plot\n",
    "    plt.xlabel(\"False positive rate (fpr)\")\n",
    "    plt.ylabel(\"True positive rate (tpr)\")\n",
    "    plt.title(\"Receiver Operating Characteristics (ROC) Curve\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "19445887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8669181034482759"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_test, y_probs_positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7a136e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot prefect ROC curve and AUC score\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_test)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ae54ba8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# perfect AUC score\n",
    "roc_auc_score(y_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16fb463d",
   "metadata": {},
   "source": [
    "### Evaluating A Classification Model 4 (Confusion Matrix)\n",
    "\n",
    "A confusion matrix is a quick way to compare the labels a model predicts and the actual labels it was supposed to predict.\n",
    "\n",
    "***In essence, giving you an idea of where the model is getting confused.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f1f6070b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23,  6],\n",
       "       [ 6, 26]], dtype=int64)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_preds = clf.predict(X_test)\n",
    "\n",
    "confusion_matrix(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b0a587d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Labels</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Labels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Labels   0   1\n",
       "Actual Labels           \n",
       "0                 23   6\n",
       "1                  6  26"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize confision matrix with pd.crosstab()\n",
    "pd.crosstab(y_test, \n",
    "            y_preds, \n",
    "            rownames=[\"Actual Labels\"], \n",
    "            colnames=[\"Predicted Labels\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3fc220d2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# INSTALL SEABORN INSIDE NOTEBOOK\n",
    "# import sys\n",
    "# !conda install --yes --prefix (sys.prefix) seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "56594ccc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make our confusion matrix more visual \n",
    "# with Seaborn's heatmap()\n",
    "import seaborn as sns\n",
    "\n",
    "# set the font scale\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "# create a confusion matrix\n",
    "conf_mat = confusion_matrix(y_test, y_preds)\n",
    "\n",
    "# plot it using seaborn\n",
    "sns.heatmap(conf_mat);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8342f649",
   "metadata": {},
   "source": [
    "### Evaluating A Classification Model 5 (Confusion Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "4f6d9b73",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fix issues about above map\n",
    "\n",
    "def plot_conf_mat(conf_mat):\n",
    "    \"\"\"\n",
    "    Plots a confusion matrix using Seaborn's heatmap().\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(conf_mat,\n",
    "                     annot=True, # annotate the boxes with conf_mat info\n",
    "                     cbar=False)\n",
    "    plt.xlabel(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\");\n",
    "    \n",
    "#     # fix the broken annotations \"happened in old version of matplotlib\"\n",
    "#     bottom, top = ax.get_ylim()\n",
    "#     ax.set_ylim(bottom + 0.5, top-0.5)\n",
    "\n",
    "plot_conf_mat(conf_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f9dcfd",
   "metadata": {},
   "source": [
    "#### Anatomy of confusion matrix\n",
    "![confusion_anatomy](documents/sklearn-confusion-matrix-anatomy.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66dfaabb",
   "metadata": {},
   "source": [
    "#### Creating a confusion matrix using Scikit-Learn\n",
    "\n",
    "Scikit-Learn has multiple different implementations of plotting confusion matrices:\n",
    "\n",
    "* `sklearn.metrics.ConfusionMatrixDisplay.from_estimator(estimator, X, y)` - this takes a fitted estimator (like our `clf` model), features `(X)` and labels `(y)`, it then uses the trained estimator to make predictions on X and compares the predictions to y by displaying a confusion matrix.\n",
    "* `sklearn.metrics.ConfusionMatrixDisplay.from_predictions(y_true, y_pred)` - this takes truth labels and predicted labels and compares them by displaying a confusion matrix.\n",
    "\n",
    "> Note: Both of these methods/classes require Scikit-Learn 1.0+. To check your version of Scikit-Learn run:\n",
    "\n",
    "\n",
    "import sklearn\n",
    "sklearn.__version__\n",
    "\n",
    "> If you don't have 1.0+, you can upgrade at: https://scikit-learn.org/stable/install.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b34f5264",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VVWhYpUOHDgwZMoSsrCwWL158STVX+KCRlJQEQFBQULHH69WrV6idVFzNn86i/956LImbRbsX/MjLNhG3wIvfni57mao1E3b/szopP+Z3wjV/KqvkORwil0k17/zwUCswl2mPN+bTBXW5cN4Lk6k6e34O5oWBTcnJNhF2VSbd+p4t42oiZVu0aBHR0dHUqVOH2bNn06tXr2LbpaSkcNVVV9G2bVsaNGhQ5HhAQAA1a9bEMAzb98/Y2FiOHj1KQEAA7dq1K3JOjx49AFi7du0l1Vzhh04Klq96exf/zajgfWeWuXqYzAR5N3f4/KrMbDoN5OFvqUuQt0/pjcOgTrVQ6oXWpePIltSOOMyagWc5vc4Cu+oRdFPxXcuZKXl8P/IcKbvyuz6uecKXa0cWHzyleM2vL+PPRhySk7UXs08uxw77c/pUG5pfDw1b5f+j3rBVAxJiYMcP6dx0WwJd++VwcF9TN1d85bFU8yQnO/cy3KlibNgVHBzM+PHjefDBB/H29mbv3r3FtmvQoAEffPBBideJj4/n3LlzeHh42H6QLxhBaNGiBaZilgoWvH/06FEyMjKoXr26XTVX+KBhNpvJy8sr9kP/meFEn1QNz7o8HDbT4fOrsjWeo7jIaTrWHUjPsK52n3dP6HPcMwAS343i9837qX/gNgY/2K9Iu/h9x4h6YCrJcTmYTCZGvj6Y+5/UXJxL9fAOd1dwZcpL+gnyThIa0ZXZO/5d6NiEJWMAMC5GY6S9yjUd/Ji941V3lHnFO3H41GW5j7NbkJeHvn37lst1Xn/9dQA6duxoGyIpawTBy8sLf39/UlNTSU5OpmHDhnbdq8IHDV9fX86dO1firNrMzPxZ3/Ymq+Kk5Z5mZcIkh8+vyi7kpgCw+fQSkg5/UuhYZkoeFxKs1Ar3xNMn/y9onWqh3BP6HJ8fm86Z7GNcDMhfLbTl4JfkHf6h0Pmnfsrmh1HnyD5vYPaCjq/7k3bHtyw8/O1l+GRXlq191aPhCk9MttK6LWxftY33Xh0H5PdkTFgyhqkD3yIh5ji39oqlz6OQcCCZ6bePc3PFV57Jn42/bPcqj8mgiYmJDBo0qMTjlzos4Yg5c+awevVqvL29GTfuj6/JixfzJzOXNIIA+WHjz23tUeGDRr169Th37hynT58uNj0VJLCCuRqOyDOsnMqMdfj8qsxq+AIenM85zanMP2Z15uXChr/nz8O45o0MgnoU7to8k32MU5mxnEvwAczkBZzjVOYf82zO/mxm52PVycs0YamVx3UzM/Bqk8apzMv0wa4wsTv1nA1X+OV7D1q3hZDGp4ndeaTQsYSY48TuPELP/okAxO2jSBtx3uUZNrlyvP3227z77rt4eHgwderUQgstzOb8vY3KGkG4VBU+aLRs2ZIDBw4QGxtL27ZtixyPjY21tZOKw8MTare3krLRk2MfWooEDYDU3R6k7s4f8wzs9MfxjGMmdv8zP2R4BeXRNjr9kpbAilwu6z+pxcAnTxHSJJuOt6ey+duahY7XDMihc69zAPz4Vc1iriCVRXk9vTUkJOSy9Fr8VXZ2NlFRUXz22Wd4enoybdo07rzzzkJtfH19gT9GCoqTlZW/2u9SRhEq/KqTTp06AbBq1aoix86ePcvWrVuxWCzcfPPNl7s0KUPTEdlgMjj7sycxU72w/mm+7snNWez+Z3UwTATfnYN/6z+W/v3+kje5aflPbW0zK0MhQyqshFhvvlmSv2zw6RlHiez2x8aB/rUzeWFOPL418ji815tNXytoVHZ5hodTL3dJSUlh8ODBfPbZZ/j5+TF37lzuueeeIu0K5macPn262OtkZWVx/vx5TCYTdevWtfv+Fb5Ho1u3boSEhLBx40aWLFnCwIEDgfzE9cILL5Cens6AAQMIDAx0c6XyV7XaWmk1MYv9U7w49kE1Tnxiwb9pCmsujuJU/DnAg8DOubR++Y/0fH6PB2e35n9Zmr0NYv5V+vMhmo7IJvDvWuYq7jPrxQbUqZdDZLc0Jv0vjjNJp8hLPsik9/ZhseRxMsHC1Mcak5tT4X+ukyvQ0aNHefjhhzl+/DihoaHMmTOHFi1aFNu2YGTg4MGDxR4/ePAghmHQqFEjfHzsn/dV4YOGt7c306dPZ8SIEUyaNIkVK1YQGhrKzp07SUpKIiIigmeeecbdZUoJQvvm4H+1laMLq3H2ZzPnDuTi559B8M3VCLw3laA7cgs9cO3s9j8ekpZzzoPUnaX/45ydYuduXyIukp3pwcQhTbn1vnPcPuAM4W2yIPcQKSd9WLfCh5Xz65J2tsL/UytlMpXDqpPLu2rl1KlTDB48mBMnTnDttdcyZ84cAgKKPvyvQOPGjWnSpAlxcXHs2rWLNm3aFDr+7bf5E/E7d+58SXVUiq/+yMhIli1bxsyZM9m2bRuxsbGEhobSp08fhg0bhp+fn7tLrLL+trrsmcf+rfO4+t/5vRZB3s15OGwmCw8/wanMlCJtGz+cQ+OHFR6ksjGx/pParP+kNs2vb8rsHa8y+c5xmvx5halIW5Db49lnn+XEiRO0bNmShQsX2uZglGbw4MFMmjSJF198kffee882WrBlyxYWLVqExWLhkUceuaQ6KkXQAAgPD+ftt992dxkiIiIV3qZNm9i6dSsANWrU4KWXXiqx7ZgxY2yrOgcMGMAPP/zAhg0b6NGjB5GRkaSlpbF9+3YMw2D69OmX9JwTqERBQ0RExF3Ka9XJ5bJ+/Xrbr7dv315q2yFDhtiChoeHBzNnzuT9999n5cqVbNy4ET8/Pzp27MjIkSNp3779JdeioCEiImKHirAz6F+NHj2a0aNHF3k/KiqKqKgoh65psVgYOnQoQ4cOdbY8oBIsbxUREZHKSz0aIiIiZTHKoUejim4JpKAhIiJih4o4dFIZKGiIiIiUwcD55a1VtENDczRERETEddSjISIiYgcNnThGQUNERKRMlW8L8opCQyciIiLiMurREBERKUNl2xm0IlHQEBERsYPmaDhGQyciIiLiMurREBERsYOhHg2HKGiIiIjYwdkNu6oqDZ2IiIiIy6hHQ0REpCx6qJrDFDRERETKYOD8HI0qmjMUNEREROyh5a2O0RwNERERcRn1aIiIiJTJVA7LW6tmj4iChoiIiB00dOIYDZ2IiIiIy6hHQ0RExA5GVV024iQFDRERkTIYOL8zaFXNKRo6EREREZdRj4aIiIgd9FA1xyhoiIiI2EGrThyjoRMRERFxGfVoiIiIlMUoh1UnVXQ2qIKGiIiIHTRHwzF2BY1x48Y5fSOTycS///1vp68jIiLiDgoajrEraHz++eeYTCYMB/qNCs5T0BAREal67AoavXr1wmRSkhMRkarJwOT0qhNDD1Ur2fTp011dh4iISIVWEbcgj4uLo1evXvTu3ZuJEycW22bz5s3Mnz+fmJgYMjMzCQsLo3///vTp06fYToTc3FxWrFjBRx99RFxcHJ6enlxzzTWMGDGCyMjIS65Ry1tFREQqoeTkZEaNGkVGRkaJbZYuXcrQoUP5+eefiYiIIDIykkOHDhEVFcWECROKtDcMg/HjxzNx4kSOHTtGx44dCQ8PZ/PmzQwZMoQVK1Zccp3lsurk0KFDbNiwgcOHD5OWlsbbb79Neno6q1at4q677sJisZTHbURERNymIk0G3bdvH2PGjCE+Pr7ENkeOHGHy5Mn4+fmxePFiIiIiAEhMTGTIkCGsXLmSTp06cfvtt9vOWbFiBV9++SWtW7dm4cKF1KpVC4AtW7YwcuRIXnnlFTp27Ej9+vXtrtWpHo2MjAzGjx/P3Xffzeuvv86KFSv47rvvADh27BjPP/88d9xxB0ePHnXmNiIiIm5nGCanXuUhNTWV1157jX79+hEfH09oaGiJbaOjo7FarQwbNswWMgBCQkJswyzR0dGFzpk7dy4AUVFRtpAB0KFDB4YMGUJWVhaLFy++pJodDhp5eXmMGjXKtiIlIiKC2rVr245nZWXh6enJsWPHGDhwIGfOnHH0ViIiIgIsWrSI6Oho6tSpw+zZs+nVq1eJbdevXw9A9+7dixzr2LEjNWrU4LfffuPUqVMAxMbGcvToUQICAmjXrl2Rc3r06AHA2rVrL6lmh4PGihUr2LJlC40bN+bTTz9lxYoVNG3a1Hb8mmuu4ZtvvqFp06YkJyezcOFCR28lIiLidoaTr/IQHBzM+PHjWbVqFV26dCmxXXJyMikpKVgsFsLCwoocN5vNtvf3798PwIEDBwBo0aJFsZNEC94/evRoqfNC/srhORqffvopJpOJ//znP7Ro0aLYNg0bNuStt97i3nvvZf369Tz11FOO3k5ERMStymP4IzExkUGDBpV4vKzegr59+9p1n6SkJAACAwPx8Ci+T6FevXqF2hb8NygoqNj2Xl5e+Pv7k5qaSnJyMg0bNrSrFod7NA4cOECjRo1o1apVqe3Cw8Np3LgxCQkJjt5KRERELkF6ejoA3t7eJbbx8vIq1PbixYt2n1PQ1h4O92hkZWXh4+NjV1tfX1+HdhUVERGpEMpj/MPIn4h5qXMcHFHQi2HPZpsF35/NZrPd51wKh4NGcHAwcXFxZGdnU61atRLbpaenc+jQoRK7YkRERCqDirS8tSy+vr4AZGZmltgmKysLwNZpcCnnVK9e3e5aHB466dixI5mZmbalMCV5++23ycrKokOHDo7eSkRExK0M8ncGdep1Gest+OE+OTm5xBGFgjkZBXM1Cs45ffp0se2zsrI4f/48JpOJunXr2l2Lwz0aw4YN49NPP2X27NlcuHCBu+++m+zsbCA/DR04cIDFixfz5Zdf4unpyZAhQxy9lYiIiFyCWrVqERQUxKlTp4iPj6dJkyaFjlutVg4fPgxAy5YtC/334MGDxV7z4MGDGIZBo0aN7J46AU70aDRs2JDXXnsNT09PFi1aRN++fdm7dy8A119/PQ888ABffPEFHh4eTJo0qdjlNSIiIpVFRdiw61J06tQJgNWrVxc5tmnTJtLS0mjVqhXBwcEANG7cmCZNmpCUlMSuXbuKnPPtt98C0Llz50uqw6mdQbt168bHH39Mly5dsFgsGIZhe3l4eNChQwcWL17Mfffd58xtRERE3M8wOfe6zAYOHIjZbGbu3LmFgkNiYiKTJ08GYOTIkYXOGTx4MAAvvvgiycnJtve3bNnCokWLsFgsPPLII5dUh9PPOmnVqhXvvvsu2dnZxMfHk5aWho+PDw0bNrRNLBEREZHLq1WrVowdO5Y33niDBx98kPbt2+Pt7c3WrVtJT0+nb9++9OzZs9A5AwYM4IcffmDDhg306NGDyMhI0tLS2L59O4ZhMH369Et6zgmU00PVAKpVq1bixl0iIiKVXWXcpWHEiBE0a9aMhQsX8uuvv2IymWjWrBkDBgwodrTBw8ODmTNn8v7777Ny5Uo2btyIn58fHTt2ZOTIkbRv3/6Sa3A6aOTk5PDtt9/ant6akZFBzZo1CQ8Pp0uXLtx6663O3kJERMT9KmDQGD16NKNHjy61TdeuXenatavd17RYLAwdOpShQ4c6Wx7gZNCIiYlhzJgxHD16tMjymd27d7N8+XLatGnDf/7zH+2jISIiUgU5HDSSkpIYMmQIqamp+Pv7c9ttt9GyZUt8fX25cOECe/fuZc2aNezcuZNhw4axbNmyS9rgQ0REpMIwymHDrgrYI3I5OBw05s2bR2pqKjfccAPvvPNOoUfEFzh58iT/+Mc/2L9/PwsWLOCJJ55wqlgRERG3qaJBwVkOL2/dsGEDFouFGTNmFBsyIH+b8nfeeQcPDw++/vprh4sUERGRysmpoZPw8PAytyFt2LAh4eHhth3IREREKp/y2HSr8jwrpTw5HDQCAgI4d+6cXW3T09OpUaOGo7cSERFxPw2dOMThoZPu3buTmJjImjVrSm23bds24uPj6dKli6O3EhERqQBMTr6qJoeDxujRo2nevDnPPvssy5cvJzc3t0ibtWvXMmbMGBo2bMjYsWOdqVNEREQqIbuGTkp6gEpmZiYZGRm8+OKLTJ8+nebNm+Pr60t6ejpxcXG2oZUGDRrw3HPPMW/evPKqW0RE5PLS0IlD7AoaJ0+eLPW4YRhcuHCh2Ke9AezZsweTqep2G4mIyBVAQcMhdgWNadOmuboOERERuQLZFTT0mHcREany3PCo9ytBuT29VURE5EpWGZ/eWhE4HTTS0tI4cuQIGRkZ5OXlFTpmtVrJzMzk5MmTrF+/ngULFjh7OxEREalEnAoa//nPf1iwYEGxS1tFRESuGAbOTwatoj0iDgeNVatWMWfOHLvaNm7cmLvuusvRW4mIiLif5mg4xOENu1asWAFAz5492bBhA1u2bMHDw4N+/fqxZ88e1qxZw8iRI/Hw8MAwDIYPH15uRYuIiEjl4HDQ2Lt3L15eXrz88ssEBwdTu3ZtwsLC2LJlC56enoSGhvLkk0/y2GOPkZCQwJIlS8qzbhERkcvKZDj3qqocDhqpqamEhobi7+9vey88PJxjx46RlpZme2/o0KFUq1aN1atXO1epiIiIOxlOvqooh4OGl5cXXl5ehd5r2LAhAIcOHbK95+fnR+PGjYmLi3P0ViIiIu5nmJx7VVEOB42goCCOHz+O1Wq1vdeoUSMADh48WKR9enq6o7cSERGRSsrhoNGuXTvOnz9faG+MFi1aYBhGoUfHnzp1iiNHjlCvXj3nKhUREXEnDZ04xOGgMXDgQEwmEzNmzKBv375kZ2dz7bXX0rhxY3744Qeef/553n//fYYPH05ubi5XXXVVedYtIiJyeSloOMThoNGqVSuioqIwm83ExsZSrVo1AEaNGoVhGHz66adMmTKFgwcP4uHhwahRo8qtaBEREakcnNoZ9MEHH+Tmm29m48aNtvfuvfde8vLymDdvHseOHSMsLIwnn3yS1q1bO12siIiIW2hnUIc5/ayTxo0b07hx40Lv3XfffXriq4iIXFmq8MoRZzg8dCIiIiJSFrt6NJYvX14uN+vTp0+5XEdERORyq8q7ezrDrqARFRWFyeR8l5GChoiIVFoKGg6xK2iEhIS4ug4RERG5AtkVNNatW+fqOkREROQK5PSqExERkaqgoszR+P7771m4cCG//fYbmZmZBAUF0blzZx577DECAwOLtN+8eTPz588nJiaGzMxMwsLC6N+/P3369CmXaRFlUdAAMo55sObqGu4uo0pofr0PD++ArX19iN2p3/PLZVXiLneXUHV4ZgPw7qr9kPu7m4u58pkCsy/fzSrA8tb33nuP6dOnYzKZuP7666lTpw6//vor77//PqtWreKDDz6wPXcMYOnSpbz88stYLBYiIyOxWCz89NNPREVF8csvvzBt2jSX16ygISIiUgkcP36cN954g2rVqhEdHU1kZCQA2dnZjBs3jm+++YYpU6Ywd+5cAI4cOcLkyZPx8/Nj8eLFREREAJCYmMiQIUNYuXIlnTp14vbbb3dp3dpHQ0RExB5uftbJli1byMnJ4eabb7aFDIBq1aoxduxYALZu3Wp7Pzo6GqvVyrBhw2whA/IXeEycONHWxtUUNEREROzh5qBhNpsBSEpKKnIsOTkZgNq1a9veW79+PQDdu3cv0r5jx47UqFGD3377jVOnTjlfXCkUNERERCqBDh06YLFY2Lt3Ly+99BLHjx8nIyODLVu28PzzzwMwYsQIID94pKSkYLFYCAsLK3Its9lse3///v0urVtzNERERMpilMOqEyN/fsSgQYNKbLJ27doSjwUHBzNjxgxeeOEFPvzwQz788EPbsdq1azNz5ky6desG/NHrERgYiIdH8X0K9erVK9TWVdSjISIiYg83D50ARERE0L17dzw9Pbn++uu59dZbqVevHmfPnmXevHkkJCQAkJ6eDoC3t3eJ1/Ly8irU1lXKpUcjLy+PvXv3cvjwYdLS0njooYfIycnh5MmTNGzYsDxuISIiUumFhISU2mtRmn379jF06FC8vLxYtmyZbYJnTk4Ob775Jv/9738ZNGgQX3/9ta0Xw559MgzDtRuEOB00VqxYwTvvvFNoMslDDz1EYmIiPXv25I477uBf//pXqalKRESkwnPzhl3/+te/OHv2LO+++26hVSQWi4Vx48axe/duduzYwfLly22rUjIzM0u8XlZWFgA+Pj4urdupoZM33niDqKgoTp48iclkss2IBTh58iRWq5WvvvqK4cOHk5ub63SxIiIi7mIynHs5Iysri19++QWTycTNN99ctDaTiU6dOgGwZ88egoKCgPxJoSX1WBTMzSiYq+EqDgeNn376ifnz5+Pt7c3LL7/Mtm3buPbaa23HIyMjefXVV6levTo7duzgo48+KpeCRUREqprz58+Tl5dX5If6Pyt4Pzc3l1q1ahEUFER2djbx8fFF2lqtVg4fPgxAy5YtXVc4TgSNxYsXYzKZmDp1Kv3798fPz69Im3vuuYdXX30VwzD44osvnCpURETEfUz5W5A788LxLcwDAgKoVasWeXl5bNiwodg2mzZtAqB169YAth6O1atXF9s2LS2NVq1aERwc7HBd9nA4aOzatYvAwEDuuOOOUtvddttt1KtXj9jYWEdvJSIi4n5uXHXi4eHBgAEDAJgyZQoHDhywHcvLy2PmzJls3rwZf39/7r//fgAGDhyI2Wxm7ty57Nq1y9Y+MTGRyZMnAzBy5EjnCrODw5NBU1NTadWqlV1tg4KC2Ldvn6O3EhERcSsTzs+zcPaRbI8//jgxMTGsX7+ee++9l7Zt21KzZk1iYmI4fvw4Pj4+vPXWW9SpUweAVq1aMXbsWN544w0efPBB2rdvj7e3N1u3biU9PZ2+ffvSs2dPJ6sqm8NBo1atWrb1uqUxDINjx44V2hZVRERELo3FYmH27NmsXLmSlStXEhMTQ1ZWFvXq1aNfv348+uijhZ7cCvk7hTZr1oyFCxfy66+/YjKZaNasGQMGDOC+++67LHU7HDTatm3Ld999x1dffcWdd95ZYrtPPvmEs2fPFrvXuoiISKVQHptulcPyWJPJxP33328bHrFH165d6dq1q/M3d5DDczQGDRqEYRhMmjSp2M1H8vLyWLZsGZMmTcJkMtG/f3+nChUREXEndy5vrcwc7tG48cYbGT58ONHR0TzxxBP4+vqSk5MDQJ8+fYiLi+PixYsYhkG/fv3o2LFjuRUtIiIilYNTO4M+88wzhIaG8s4775CSkmJ7f8+ePQDUqFGDESNG8OijjzpXpYiIiLtV4V4JZzi9BXn//v25//772blzJwcPHiQtLY3q1avTtGlTbrzxRqpXr14edYqIiLiXgoZDyuWhahaLhfbt29O+ffvyuJyIiIhcIcolaIiIiFzpqvKETmc4HDQudamMyWRizZo1jt5OREREKiGHg8bx48ftamcymTAMA5PJ2T3RREREpLJxOGhMmzatxGPp6ekkJSWxbt06YmNj+ec//8ldd93l6K1ERETcT0MnDnE4aNizdemYMWN4/vnnmTVrFn//+98dvZWIiIjbaY6GYxzeGdSui3t48MILL+Dp6cmcOXNceSsRERHXcuPTWyszlwYNAH9/f8LCwtixY4erbyUiIiIVzGVZ3nr27FkyMjIux61ERETKXwV5qFpl5PKgsXjxYk6cOEGLFi1cfSsRERGX0RwNxzgcNMaNG1fiMcMwyM7O5vDhw8TGxmIymbTqREREpApyOGh8/vnntj0yynLDDTcwdOhQR28lIiLifurRcIjDQaNXr16lbsJlNpupXbs27dq1o1OnTtqwS0REKjUNnTjG4aAxdepUPDxcvmhFREREKjGHk8IjjzzCs88+y/nz58uzHhERkYpJ+2g4xOEejT179lC9enX8/f3Lsx4REZGKqQqHBWc43KNhtVoJCAgoz1pERETkCuNw0OjatSsHDhzQjp8iIlIlmAznXlWVw0Mnzz//PImJiTzyyCPccccdtGvXjrp16+Ll5VXiOR06dHD0diIiIu6jnUEd5nDQ+Nvf/mb79WeffcZnn31WanuTycTvv//u6O1ERETcq4oGBWc5HDTs2ajLmfYiIiJS+TkcNGJiYsqzDhERkQqtKs+zcIZdk0EHDx7MlClTXF2LiIhIxaV9NBxiV4/Gtm3bsFqtrq5FRERErjAuf0y8iIhIZWfC+aGTqvrELwUNERERe1Th4Q9n6KloIiIi4jLq0RAREbFHBenROHPmDPPnz2fdunUkJibi7e3Ntddey/Dhw4vdGHPz5s3Mnz+fmJgYMjMzCQsLo3///vTp0weTyfUDOnYHjT179tC1a1eHb2QymVizZo3D54uIiLhTRZhjcfjwYYYMGUJSUhINGjSgU6dOHDt2jI0bN7Jp0ybefvttunfvbmu/dOlSXn75ZSwWC5GRkVgsFn766SeioqL45ZdfmDZtmstrtjtoZGdnc/z4cYdvdDlSk4iIyJUqNzeXp556iqSkJAYPHsxzzz2H2WwG4NNPP2X8+PE899xzdOrUCS8vL44cOcLkyZPx8/Nj8eLFREREAJCYmMiQIUNYuXIlnTp14vbbb3dp3XYHjfr169O7d29X1iIiIlJxuXno5LvvvmPfvn20a9eOCRMmFPoBvlevXnz11VccOXKEffv20aZNG6Kjo7FarQwbNswWMgBCQkKYOHEiw4cPJzo6umIFjSeeeMKVtYiIiFRM5fEEVifP/+abbwAYNmxYsaME8+fPL/T/69evByg0lFKgY8eO1KhRg99++41Tp04RFBTkXHGl0GRQERERe7i5R2PPnj0AXH/99Zw7d46vv/6amJgYPD09ueGGG+jRo4dtKCU5OZmUlBQsFgthYWFFrmU2mwkLC2P37t3s379fQUNERKQqK5gn6enpyf79+3nyySc5e/as7fiSJUuIiIhgzpw5BAUFkZSUBEBgYCAeHsXvZFGvXj0AW1tXUdAQERGxRzn0aCQmJjJo0KASj69du7bY9y9cuJBfgmEwatQorrnmGsaNG0fTpk05cOAAU6ZM4bfffuOxxx5j2bJlpKenA+Dt7V3ivby8vABsbV1FG3aJiIjYwWQ493JGdnY2AFarldDQUBYsWMDVV1+Nr68v119/Pe+99x5169Zl7969rF692taLYc+KT8Nw7ZiQXT0a06ZNIyAgwKWFiIiIXOlCQkJK7LUozZ97JgYMGIDFYil0vEaNGtx9993897//ZcuWLQwcOBCAzMzMEq+ZlZUFgI+PzyXXcynsChr33XefS4sQERGp8Nw4GbRGjRpUq1aN7OxsQkNDi21T8P7Zs2dtkzuTk5MxDKPYno2CuRkFczVcRUMnIiIidnDn0InZbKZFixYAnDp1qtg2ycnJANSpU4datWoRFBREdnY28fHxRdparVYOHz4MQMuWLZ0rrgwKGiIiIpVA586dAfj888+LHDMMgx9++AGAyMhIADp16gTA6tWri7TftGkTaWlptGrViuDgYBdVnE9BQ0RExB6Gky8n9e/fH39/f7Zt28bMmTNtkzgNw+Dtt99mz549NGzYkNtuuw2AgQMHYjabmTt3Lrt27bJdJzExkcmTJwMwcuRI5wsrg5a3ioiI2MHpnUGdVK9ePd544w1Gjx7NO++8w2effUbLli05cOAA8fHx1KxZkzfffJNq1aoB0KpVK8aOHcsbb7zBgw8+SPv27fH29mbr1q2kp6fTt29fevbs6fK6FTREREQqiVtuuYXPP/+cOXPmsHnzZjZs2EBgYCB9+/Zl5MiRNGzYsFD7ESNG0KxZMxYuXMivv/6KyWSiWbNmDBgw4LIt9FDQEBERKUt5DH+UU49I48aNL+nx7l27dqVr167lc3MHKGiIiIjYw81DJ5WVgoaIiIgd3D1Ho7LSqhMRERFxGfVoiIiI2EM9Gg5R0BARESmTgcnph49VzaSioRMRERFxGfVoiIiI2KNqdkg4TUFDRESkDCacX3VS9PmpVYOGTkRERMRl1KMhIiJSlgq0M2hlo6AhIiJiB23Y5RgNnYiIiIjLqEdDRETEHurRcIiChoiIiB00dOIYBQ0RERF7KGg4RHM0RERExGXUoyEiImIHDZ04RkFDRETEHk4/VK1qUtCQy+K6m9O4Z2gK19wUQ97Jr5iyyMyODd4sfSuIhFhvd5cnUqKvFgfw9viGjH3tKHcMPFPkeG4O3Nv8WnJz/jwS3ReoBrQBwNffysqY34qcu2erLyvm1mXvz75kXDQTFJpN+67n6T3iNIH1c1zyeUQuNwUNcblHJiTywBOnATiX4g2eYfjWOEjX+zP5W89UXh7alF9+qOHmKkWK2r+rOtGTQ0ptE3/Am9wcD7yqW2l+dQaYfMDSGnL2gZEOQHW/vCLnfTSzHu9Nq49hmKjua6VJy0xOJ1pYMbceqz6sw8QFcVzX8YJLPpc4RkMnjlHQEJfq3j+FB544TU62ibfGhXLkYFtm73iNCZ3G0mf4j0R2S2PcO0cZclMrsjLM7i5XxGb3Zj8mDW9C+oXSvy4P/14dgGtuusiUJYfBMwKPwH+Rl3wv5MYWe85Pq/3579T8AHPHwGT+8Uoi3j555OXBirl1iZ7cgBcfCmPehhiCG2WX7wcTx2gLcodVylUncXFxtGnThkmTJrm7FCmFxSuPR6NOADBnYgjffVyHgucXpl+oxvQnGnMxzYPadXPp0OO8GysV+UN2ponFrwfz3APNuHCu7J/FCoJG01YZdt9j8evBALTrdJ6xrx3D2ye/x8PDA/o+dprO954lK9ODuS+X3psiUhlUuh6N5ORkRo0aRUaG/X+pxT1u6nYe/zpWjh2qxtfvBxQ5np5mZlZUA/xrWzl2yMsNFYoUdvxINcb3bc7pxGp4mA2GjD/Bt0sCOHWsWonnHN6bHzQat8q06x5nkjyJ3eMDQN9RScW2ue/R02z4rDY/fVeT82fN+Ne2XuInEVcwFR0BEztUqqCxb98+xowZQ3x8vLtLETu0vSUNgC2ra5KXZyq2zZpldS5nSSKlSj5RjdOJ1Wjd7iKPTzlGi2sz+HZJ0ZD8Z0dsPRr2BY2kP4WWFtcW/wNTaLMsAPKsJg7u9qFd5zS7ri0uVkWHPpxVKYJGamoq8+bNY9GiRWRnZxMaGsqxY8fcXZaUoWnr/H944/d7AwY335FK9wE7yDszhGHPxbPhUw/WLKuDNbf4ECJyuQXWz2by4kO072rfN/aUk56knvHEw2xQ3c/KB28FsX+XJ1m5kwiqb+Zvt9fgxi5/udafvtw9LcV/58rN/ePXpfWmiFQGlSJoLFq0iOjoaIKDg3nppZfYu3cvM2fOdHdZUoZ6ofmT2HJzTLy+8hDX3HQx/0B2Am3/Dm3/Dr2GJTNxcFNOJ+ofU3G/Bk2zadDU/smXBfMzTCb4R9dWZGcWTHv7DTDz7fvNaH9bKs/Pisfn/1eeBDfKsp1/aE91rmp/sch14/dXt/067ZwmSVcUWnXimEoxGTQ4OJjx48ezatUqunTp4u5yxE4F/7D+45VEwq9LZ/aLIYwfcAemoN3MfvkmTh6tRlhEJpP+dwRPiwY/pfIpmJ9hzTUR2S2Vmd/u54uj2Xx8Yj5PTM/Fx8/KtjU1mfqPJrZzagVYad0uP1x88FZQsXtALX0ryPbr3Bz1+FUYhuHcq4qqFEGjb9++PPLII3h7a2OnyqSad354qBWYy7THG/PpgrpcOO+FyVSdPT8H88LApuRkmwi7KpNufc+6uVqRS9c0IoO7hiQz6JkTRM2Np8W1GVTzhtpBtbj7kTwmLz6MyWTw8zp/fl73x14xQ58/gYeHwfb1/rzySFPiYrzJzYHjh6vx6j8bsXuTH3618sdPPD2r7jeoisREfo+GUy93fwg3qRRDJ65m9jTT/Pqm7i7jipOTtRezTy7HDvtz+lQbml8PDVs1APL/mxADO35I56bbEujaL4eD+/Rn4DKe2ovBYSZL/n/NIeAZXOhQ+x75r3z/P2nUHGb779U3w/W3GPzyvYlNq5pwY/f81SPX3QJP/cfKW8+Y2bKqJltW1bRds5q3wdg3rXy1yIP9v4BPzXrgGejCD1jJmSxgaBfVikxBA6jbMIDZO151dxlXnLyknyDvJKERXZm949+Fjk1YMgYA42I0RtqrXNPBT38GUjF5jAJOY/J7Ao/ArvafVnsGAM3bv88v33/GqRNt8AicaDve4wm4tucpvp6/hthdR/Awe9C8TVNuH9aF+k2DWDIj/751W07AI/DGcv5QVxYjN+Ey3ARt2OUgBQ3gdEIKL92nb3Ll7YnJVlq3he2rtvHeq+OA/J6MCUvGMHXgWyTEHOfWXrH0eRQSDiQz/fZxbq74yvXuqv3uLqHyyrMAJowLM8lLfrvIYasVMMBc8K+pOQyP2jPIO/skWA+Td9EMmDEbu/N3C/2TIH8Y+vSf39kKfMi5A3AqPn+CdOMG/yIvufw/1pXCVHvu5btXFQ0KzlLQAKy5VmJ3HnF3GVecX773oHVbCGl8usjvb0LMcWJ3HqFn/0QA4vahPwNXyv3d3RVUXkYEUA2siZBb+KFqg9tHkHTcwqMTE7l/5OnC51kPQ+7vxP7aDKhBo+bJkJv/9b7hs1qcSqjG3+48V+wqly1f1wEaEdQwi/qh+yC3SBMpoGET/vnPf7Jq1SqmTZtG7969ixzfvHkz8+fPJyYmhszMTMLCwujfvz99+vTBZHL9zJFKMRlUKqf1n9QCIKRJNh1vTy1yvGZADp17nQPgx69qFjkuUtE1Cs/EMEysW1EbazFhIGanD7s3+QHQudcfE56/WRLAf6eGsOrDopuBZWeZWD67HgC9hqsro0KpgKtOli1bxqpVq0o8vnTpUoYOHcrPP/9MREQEkZGRHDp0iKioKCZMmOCSmv5KQUNcJiHWm2+W5O/8+fSMo0R2+yNs+NfO5IU58fjWyOPwXm82fa2gIZVPv8dPYTIZxO7xYcYzjbiY9sc/qbs3mXhlaFMMw0TX+88Qft0fu4B2fyC/Z+ST+XX55Qc/2/tnT3sy+dEmHD3oTf0mWdw1WEGjInF21Ul5O3LkCFOnTi31+OTJk/Hz8+Pjjz9mwYIFzJkzh6+//ppGjRqxcuVKvv322/Iv7C80dCIuNevFBtSpl0NktzQm/S+OM0mnyEs+yKT39mGx5HEywcLUxxqTm6PMK5XPtR0u8o9XjjP3lQZ893Edfvi8FqHN4eLFxzl5JH+1SvuuqYx9rfBkxS69z7JltT8/flGb5/s3p36TLKr75JEQ60VOtgdBodn8++NYqnlpUoAULzs7m6effhoPDw8iIiL4/feiw6PR0dFYrVaGDRtGRESE7f2QkBAmTpzI8OHDiY6O5vbbb3dprQoa4lLZmR5MHNKUW+87x+0DzhDeJgtyD5Fy0od1K3xYOb8uaWf1ZSiVV6/hybS+4SIr59Vjz1Zf4vdbqF4jnTZ/z6N7vwS69D7LX4fBTSaYMDueb/9+gW+X1iEuxps8q4n6jbPpeMc5+vzjNDVq6UFqFU4Fyn0zZsxg7969vPrqq6xatarYoLF+/XoAunfvXuRYx44dqVGjBr/99hunTp0iKCioSJvyon/h5TIwsf6T2qz/pDbNr2/K7B2vMvnOcZr8KZXCom1lT6Rt2SaD52f9/8MePSPwCHwvf4VJbskb0Xl4QM+HUuj5UEp5lSouVlFWnWzevJn33nuPO++8k3vvvbfYORrJycmkpKRgsVgICwsrctxsNhMWFsbu3bvZv3+/gsZfjR49mtGjR7u7DBERkcvqzJkzjBs3juDgYF5++eUS2yUlJQEQGBiIh0fxQ9P16tUr1NZVKmXQEBERuezynO/SSExMZNCgQSUeX7t2bannv/DCC6SkpLBw4UL8/f1LbJeeng5Q6qM7vLy8CrV1FQUNERGRslSAnUGXLFnCunXrePTRR4mMjCy1bUEvhj37ZBgufuCbgoaIiIgdymOORkhISJm9FsU5ePAgr776KldddRVjxowps72vry8AmZmZJbbJysoCwMfH55LruRQKGiIiIhXc66+/TmZmJt7e3jz//POFju3duxeAjz/+mM2bN3PjjTfSo0f+0/6Sk5MxDKPYno2CuRkFczVcRUFDRETEHi4eYihNwTyKHTt2sGPHjmLb7Ny5k507d+Lp6ckDDzxAUFAQp06dIj4+niZNmhRqa7VaOXz4MAAtW7Z0ae0KGiIiInZw5/LWxYsXl3hs1KhRrF27tsizTjp16sTHH3/M6tWrGTFiRKFzNm3aRFpaGq1atSI4ONhldYO2IBcREbkiDRw4ELPZzNy5c9m1a5ft/cTERCZPngzAyJEjXV6HejRERETsUUE27LJXq1atGDt2LG+88QYPPvgg7du3x9vbm61bt5Kenk7fvn3p2bOny+tQ0BAREbGDyY1zNBw1YsQImjVrxsKFC/n1118xmUw0a9aMAQMGcN99912WGhQ0REREKrFZs2aVerxr16507dr1MlVTlIKGiIhIWQwgrxyuUQUpaIiIiJTJKIehk6qZNLTqRERERFxGPRoiIiL2qJodEk5T0BAREbFHJVx1UhEoaIiIiJTBhPM7g5b9HNUrk+ZoiIiIiMuoR0NERMQeGjpxiIKGiIiIHUzO7qNRRWnoRERERFxGPRoiIiJlMXB+6KSKjrwoaIiIiNijigYFZ2noRERERFxGPRoiIiJ2qIyPia8IFDRERETsoaDhEA2diIiIiMuoR0NERMQe2kfDIQoaIiIidtAcDccoaIiIiJRF+2g4THM0RERExGXUoyEiIlImoxxWnVTNLg0FDREREXtoMqhDNHQiIiIiLqMeDRERETto1YljFDRERETsoaDhEA2diIiIiMuoR0NERMQe6tFwiIKGiIiIPRQ0HKKgISIiUhYD55e3VtGcojkaIiIi4jLq0RAREbGDlrc6RkFDRETEHgoaDlHQEBERqUQ+++wzli9fTkxMDBkZGQQEBHDTTTcxYsQImjVrVqT95s2bmT9/PjExMWRmZhIWFkb//v3p06cPJpPJ5fVqjoaIiEiZDMhz8uXkbFDDMHj66acZN24cv/zyC82aNeOWW27BbDbz6aef0rt3bzZu3FjonKVLlzJ06FB+/vlnIiIiiIyM5NChQ0RFRTFhwgSn6rGXejRERETs4eahk88//5wvv/ySunXrEh0dTatWrQCwWq28/fbbzJkzh3HjxvHdd9/h6+vLkSNHmDx5Mn5+fixevJiIiAgAEhMTGTJkCCtXrqRTp07cfvvtLq1bPRoiIiKVwPLlywF4+umnbSEDwGw2M3bsWFq0aEFKSgqbNm0CIDo6GqvVyrBhw2whAyAkJISJEyfa2riagoaIiIg9DMO5l5P8/f1p1qwZN9xwQ5FjJpOJpk2bAnDq1CkA1q9fD0D37t2LtO/YsSM1atTgt99+s7V3FQ2diIiIlMXA+bDg5OnvvvtuicesVit79+4FoH79+iQnJ5OSkoLFYiEsLKxIe7PZTFhYGLt372b//v0EBQU5V1wpFDREREQuk8TERAYNGlTi8bVr1zp03Q8++IDjx49Tq1YtOnToQHx8PACBgYF4eBQ/eFGvXj0AkpKSHLqnvRQ0RERE7JFXMffR2LJlC6+++ioAzzzzDL6+vqSnpwPg7e1d4nleXl4AtrauoqAhIiJiD8PZh53kT8R0tNeiOOvXr2fs2LFkZ2czYMAA+vbtC2DrxbBnnwzDxatpFDRERETsUcF2Bl28eDHTpk3DarUycOBAXnzxRdsxX19fADIzM0s8PysrCwAfHx+X1qmgISIiUonk5uYyadIkPvroI0wmE08++ST/+Mc/CrUpmNyZnJyMYRjF9mwUzM0omKvhKgoaIiIiZTLKYY6G8z0imZmZPP7442zcuJHq1aszffr0YjfcqlWrFkFBQZw6dYr4+HiaNGlS6LjVauXw4cMAtGzZ0um6SqN9NERERMpSsLzVqZdzJVitVlvICAgIYNGiRaXu6tmpUycAVq9eXeTYpk2bSEtLo1WrVgQHBztXWBkUNERERCqB2bNns3HjRnx8fPjf//7HtddeW2r7gQMHYjabmTt3Lrt27bK9n5iYyOTJkwEYOXKkK0sGNHQiIiJiHzdOBk1NTWXBggVA/pyKuXPnltj27rvvplOnTrRq1YqxY8fyxhtv8OCDD9K+fXu8vb3ZunUr6enp9O3bl549e7q8dgUNERERe7gxaGzbts2230VcXBxxcXEltm3durVt2KTg0fELFy7k119/xWQy0axZMwYMGMB99913OUpX0BAREanounXrxv79+x06t2vXrnTt2rWcK7KfgoaIiIg98pzfsKsqUtAQERGxRwXbsKuy0KoTERERcRn1aIiIiNhDPRoOUdAQEREpi1EOO4NW0aCioCEiImIHoxye3loVaY6GiIiIuIx6NEREROzh9EPVqiYFDREREXtU0TkWztLQiYiIiLiMejRERETsoZ1BHaKgISIiUhbDcH7opIoOvWjoRERERFxGPRoiIiJ2MDR04hAFDREREXtU0aEPZ2noRERERFxGPRoiIiL20IZdDlHQEBERsYeedeIQBQ0REZGyGGA4/fTW8imlstEcDREREXEZ9WiIiIiUySiHoZOq2aWhoCEiImIHp4dOqigNnYiIiIjLmAxDO5Dk5uRyOiHF3WVUCZZqngSGBpB8LIWc7Fx3l1NlBDfKdncJVYfJgskcjGE9CUaOu6u58pnrA7mYTN4uvY0110rS0WSnrlGvUSBmT3M5VVR5KGiIiIiIy2joRERERFxGQUNERERcRkFDREREXEZBQ0RERFxGQUNERERcRkFDREREXEZBQ0RERFxGQUNERERcRkFDREREXEZBQ0RERFxGQUNERERcRkFDREREXEZBQ0RERFxGQUMuiyNHjvDMM89w6623cu2119K9e3dmzJhBenq6u0sTKXdxcXG0adOGSZMmubsUEbdT0BCX+/XXX+nduzdffPEFgYGBdO7cmfT0dObMmUP//v25cOGCu0sUKTfJycmMGjWKjIwMd5ciUiEoaIhL5ebm8tRTT5Gens6UKVNYtmwZb7/9NmvWrKFLly7s37+fN998091lipSLffv28eCDD3Lo0CF3lyJSYShoiEt99dVXJCQk0KFDB/r06WN739vbm6lTp+Lj48PHH39MamqqG6sUcU5qaiqvvfYa/fr1Iz4+ntDQUHeXJFJhKGiIS61btw6Abt26FTlWu3ZtIiMjycnJ4ccff7zcpYmUm0WLFhEdHU2dOnWYPXs2vXr1cndJIhWGgoa41IEDBwBo2bJlscebN28OQExMzGWrSaS8BQcHM378eFatWkWXLl3cXY5IheLp7gLkypaUlARAUFBQscfr1atXqJ1IZdS3b193lyBSYalHQ1yqYPmqt7d3sccL3tcyVxGRK5OChriU2WwGwGQyldrOMIzLUY6IiFxmChriUr6+vgAl7imQmZkJQPXq1S9bTSIicvkoaIhLFczBOH36dLHHC+ZmFLQTEZEri4KGuFTBapPY2Nhijxe8X9KqFBERqdwUNMSlOnXqBMCqVauKHDt79ixbt27FYrFw8803X+7SRETkMlDQEJfq1q0bISEhbNy4kSVLltjez8zM5IUXXiA9PZ0+ffoQGBjoxipFRMRVtI+GuJS3tzfTp09nxIgRTJo0iRUrVhAaGsrOnTtJSkoiIiKCZ555xt1lioiIi6hHQ1wuMjKSZcuW0aNHDxITE9mwYQM1atRg1KhRLF68GD8/P3eXKCIiLmIytIGBiIiIuIh6NERERMRlFDRERETEZRQ0RERExGUUNERERMRlFDRERETEZRQ0RERExGUUNERERMRlFDRERETEZRQ0pMI6duwYLVu2LPF19dVXc9NNN9G/f3/mzJnDhQsX3F2yzdatW2115ubm2t5/5513aNmyJQMGDCiX+2RkZHDs2LFyuVZZSvpMpSnvz1tw/82bN5fL9UrjyOcVkaIUNKRSCA8Pp23btoVerVq1wsfHh507dzJjxgzuvvtu4uPj3V3qZfPFF1/Qo0cPtmzZ4u5SRERKpIeqSaUQFRVFZGRksce2bt3KqFGjSExMZPz48Xz44YeXuTr7DRw4kJ49e1K9enWnrzVjxgxOnTpVDlWJiLiOejSk0ouMjOSpp54CYOfOnezZs8fNFZWsTp06NGvWjJCQEHeXIiJyWShoyBWhW7dutl/v3r3bjZWIiMifaehErgg1atSw/frixYu2Xw8aNIht27Yxb9489uzZw5IlS7h48SINGzbkrbfeolmzZgAkJyfz3//+lw0bNnD8+HE8PDwICwvjzjvvZODAgXh5eRV7361bt/Lee++xZ88e0tLSaNGiBQ8//DB169Yttv0777zDzJkzadu2LUuXLi1yfN26dSxbtoy9e/dy5swZatWqxQ033MDw4cO5+uqrC12jQFRUFFFRUTzxxBOMHj3a9v7l+kyOOn/+PB9++CHff/89sbGxXLhwgerVq9OoUSNuvfVWBg8eTM2aNUs8//PPP+d///sfsbGxeHt706ZNGwYPHszNN99cbPvs7GyWLl3K119/TWxsLDk5OdSvX5/OnTszbNgw6tWrV66fT0TyKWjIFeHPk0CDg4OLHJ8zZw6//PILjRo1okaNGly4cIEmTZoAsGPHDkaNGsW5c+ewWCw0adIEwzDYu3cve/bs4bPPPiM6OrrIN9p58+bx5ptvYhgGAQEBNG/enLi4OJ5++mnat29/SfVbrVaef/55PvvsMwDq1q1LeHg4CQkJfPPNN3z33XfMmjWLTp06Ub9+fdq2bcuePXvIzs6mcePGBAQEUL9+fdv1KsJnKk1cXBwPP/wwJ06cwNPTk0aNGtGgQQOOHz/O3r172bt3L1999RUrVqzA19e3yPmzZs3i559/xtfXl+bNm5OYmMiGDRvYsGEDo0eP5oknnijUPikpiREjRrBv3z5MJhMhISHUqlWL2NhYFi5cyKeffsqsWbNo165duX1GEfl/hkgFlZCQYISHhxvh4eHGTz/9VGrbcePGGeHh4cZVV11lnD592vb+Qw89ZLvGvHnzbO+npKQYhmEYJ0+eNNq3b2+Eh4cbUVFRRmpqqq1NfHy80bdvXyM8PNx48MEHC91v+/btRnh4uNGyZUtjwYIFhtVqNQzDMDIzM43Jkyfb7hkeHm7k5OTYznv77beN8PBwo3///oWuN3fuXCM8PNy47rrrjC+//NLIy8uzXe+ll14ywsPDjTZt2hjnzp2znXPrrbca4eHhxscff1zoWpf7M5WmpM9b8OfSr18/49SpU7b38/LyjE8++cRo1aqVER4ebrz//vuFzvtzDRMmTDAuXrxoGIZh5ObmGrNmzbId27x5c6FrPvDAA0Z4eLgxYMAA49ChQ7Zj58+fN55//nkjPDzciIyMNJKSkmzHfvrpp0v+vCJSlOZoSKWVmZnJ77//zksvvcSnn34KwMMPP0xgYGCRtg0aNGD48OG2/69Tpw4ACxYs4Ny5c3Tp0oXJkyfj7+9va9OoUSNmzZqFn58f27dv5/vvv7cdmz17NgD33XcfjzzyCB4e+X+VvLy8iIqK4qabbrL7c2RnZzNv3jwAxo0bx5133onJZLJdb+LEiTRt2pT09HS++eabMq9XET5TaVJSUjh48CAAkydPLjRkYTKZ6NWrl633ZP/+/cVeo23btvzrX//Cx8cHALPZzGOPPcY999wDwNy5c21t165dy86dO6lXrx7R0dGEhYXZjtWoUYMpU6Zw3XXXcfbsWRYuXFgun1FE/qCgIZXC4MGDi2zYdd1113HffffZlrP27duXMWPGFHv+9ddfb/vm/Wdr1qwBsH2D+qvAwEDbmP/69euB/E2ytm7dCuR/Uy5O//797f5s27dvJy0tDYvFQu/evYsc9/DwYN68eWzYsIEHHnigzOtVhM9UmoCAAH766Sd2795NeHh4keNWqxU/Pz8gP0wWZ+DAgcX+efbr1w+An3/+mfT0dOCP34/bbrvNFkz+zGQy2X6vCn4/RKT8aI6GVArh4eG2bz6Q/83By8uLWrVq0bJlS2677TaaN29e4vnFTWS8ePEix48fB/LH/BctWlTsuQVtDh8+DEBiYiLZ2dkAtGjRothzWrdubcenylcwv6Rp06Z4e3sX26ZRo0Z2XauifCZ7eHt7c+LECXbv3s3Ro0dJSEjg0KFD7Nu3zxYS8vLyij03IiKi2PdbtmwJQG5uLvHx8bRu3ZoDBw4A+SEiJiam2PPOnz8P5M8dMQyj2BAjIo5R0JBKobQNu+xR3AqLP29ZXvDNqDRpaWkApKam2t4rbqIiUGi4oiznzp0DKPan7UtVUT5TWQ4fPsyrr77K999/XyhM+Pn5ccMNN5CUlFRiKCitxj+/n5GRAfzxe3LixAlOnDhRal1Wq5WLFy8WCrUi4hwFDamy/rw75xdffFFsN35xatWqZfv1hQsXbPM9/iwrK+uS6/jzslxHVZTPVJqUlBQeeughUlJSCAkJoV+/fkRERBAWFkZoaCgmk4mnn3661KBR0OPxVwXBCbAtjS34PXnxxRd56KGHyuUziIj9NEdDqix/f3/bxNHY2NgS2+3fv599+/bZfuoPCQmxDXH8/vvvxZ5TMNnRHk2bNgXyh1BK+ma+dOlSHn74YRYsWFDqtSrKZyrNihUrSElJoVatWqxYsYLHHnuMTp060bBhQ9uQRVlbqxcM+fxVQe1eXl624aaC39/S6j9x4gS7du0iKSnpkj+PiJROQUOqtM6dOwPw/vvvFzsfIC0tjSFDhtCrVy/+97//AflzC2655RaAYjfdAli2bJndNbRr1w4fHx+ys7P54osvihzPy8tj+fLlbNmypdBP8gXflA3DqHCfqTQFT5sNCQkptuckNjaWXbt2AflDGcVZsWJFse8vXrwYgFtuuQWLxQLArbfeCsDXX39NSkpKsedNmDCBBx54wLaVvYiUHwUNqdJGjBiBj48PO3bs4Nlnn+XMmTO2Y8ePH2fEiBGcPXuWGjVqMHDgQNux0aNHY7FYWLNmDa+99pptImVOTg5vvfUWq1evtrsGPz8/Hn74YQCmTZvGunXrbMcyMzOZMmUKe/bswc/Pr9Cqk4I5HQUTOyvSZypNwfLSmJgYVq1aZXvfMAx++OEHhg8fTk5ODvDHPIu/Wrt2LW+++aatxuzsbP7973+zbt06LBYLjz/+uK1tz549CQ8P5/z58wwbNqxQz8aFCxd4+eWX2bx5MyaTiREjRpTLZxSRP2iOhlRpjRs35j//+Q9PPvkkX375JatWraJ58+bk5OQQFxdHbm4uPj4+zJs3j4CAANt54eHhTJ06lQkTJhAdHc2yZcto1KgRCQkJnDt3jm7duvHdd9/ZXcfjjz/OkSNH+Oabb3jssceoX78+derUIS4ujosXL+Lt7c2bb75ZaM+JiIgIDhw4QHR0ND/88APdunVj1KhRFeYzlaRPnz588MEHxMfH889//pMGDRpQu3ZtTpw4QUpKChaLhfbt27Nt27YSh1B69OjB3Llz+eijjwgNDSUhIYHU1FQsFgvTpk0rtELGYrEwa9Yshg8fzr59+7jrrrto2rQp1atXJy4uztZL9Pzzz9t6dUSk/ChoSJXXqVMnvvrqKxYuXMiPP/7IkSNHsFqtNGjQgJtvvplHHnmEhg0bFjnvnnvuoUWLFkRHR/Pzzz+zf/9+GjduzOjRo+nateslfVP29PRkxowZdO/eneXLl7N37172799PQEAAPXr0YMSIEba5BgXGjx9PRkYGmzdv5vDhwxw6dKhCfaaS+Pn5sXz5cubPn8/69es5duwYycnJBAcH07lzZ4YMGYKPjw+33XYbMTExJCYmFnnabcEqpKVLl3LgwAH8/Pzo2bMnI0eOpFWrVkXu2bBhQz755BOWLl3KqlWrOHToEJmZmdSuXZu///3vDBo0iBtvvNHpzyYiRZmMvw7wioiIiJQTzdEQERERl1HQEBEREZdR0BARERGXUdAQERERl1HQEBEREZdR0BARERGXUdAQERERl1HQEBEREZdR0BARERGXUdAQERERl1HQEBEREZdR0BARERGXUdAQERERl1HQEBEREZf5PxzhDqQ9+8VgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "ConfusionMatrixDisplay.from_estimator(estimator=clf, X=X, y=y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "70833fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix from predictions\n",
    "ConfusionMatrixDisplay.from_predictions(y_true=y_test, \n",
    "                                        y_pred=y_preds);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe13b293",
   "metadata": {},
   "source": [
    "### Evaluating A Classification Model 6 (Classification Report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "eaa9cb82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.79      0.79        29\n",
      "           1       0.81      0.81      0.81        32\n",
      "\n",
      "    accuracy                           0.80        61\n",
      "   macro avg       0.80      0.80      0.80        61\n",
      "weighted avg       0.80      0.80      0.80        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "77e02c86",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.99990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.499950</td>\n",
       "      <td>0.99980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.99990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.99995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.499975</td>\n",
       "      <td>0.99985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>9999.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0.0  1.0  accuracy     macro avg  weighted avg\n",
       "precision     0.99990  0.0    0.9999      0.499950       0.99980\n",
       "recall        1.00000  0.0    0.9999      0.500000       0.99990\n",
       "f1-score      0.99995  0.0    0.9999      0.499975       0.99985\n",
       "support    9999.00000  1.0    0.9999  10000.000000   10000.00000"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example; look 10000 people for one has heart_disease\n",
    "# and rest of no disease\n",
    "# where precision and recall become valuable\n",
    "disease_true = np.zeros(10000)\n",
    "disease_true[0] = 1 # only one positive case\n",
    "\n",
    "disease_preds = np.zeros(10000) # model predicts every case as 0\n",
    "\n",
    "pd.DataFrame(classification_report(disease_true,\n",
    "                                   disease_preds,\n",
    "                                   output_dict=True,\n",
    "                                   zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "817972f5",
   "metadata": {},
   "source": [
    "To summarize classification metrics:\n",
    "\n",
    "* **Accuracy** is a good measure to start with if all classes are balanced (e.g. same amount of samples which are labelled with 0 or 1).\n",
    "\n",
    "* **Precision** and **recall** become more important when classes are imbalanced.\n",
    "\n",
    "* If false positive predictions are worse than false negatives, aim for higher precision.\n",
    "\n",
    "* If false negative predictions are worse than false positives, aim for higher recall.\n",
    "\n",
    "* **F1-score** is a combination of precision and recall."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1029d50b",
   "metadata": {},
   "source": [
    "### Evaluating A Regression Model 1 (R2 Score)\n",
    "\n",
    "* **R^2 (pronounced r-squared) or coefficient of determination**\n",
    "> Compares your models predictions to the mean of the targets. Values can range from negative infinity (a very poor model) to 1. For example, if all your model does is predict the mean of the targets, its R^2 value would be 0. And if your model perfectly predicts a range of numbers it's R^2 value would be 1.\n",
    "\n",
    "* **Mean absolute error (MAE)**\n",
    "> The average of the absolute differences between predictions and actual values. It gives you an idea of how wrong your predictions were.\n",
    "\n",
    "\n",
    "* **Mean squared error (MSE)**\n",
    "> The average squared differences between predictions and actual values. Squaring the errors removes negative errors. It also amplifies outliers (samples which have larger errors)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66958d4",
   "metadata": {},
   "source": [
    "**R^2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "bea167ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X = boston_df.drop(\"target\", axis=1)\n",
    "y = boston_df[\"target\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "model = RandomForestRegressor()\n",
    "\n",
    "model.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "9084b094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8654448653350507"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7e210d15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.220446049250313e-16"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Fill an array with y_test mean\n",
    "y_test_mean = np.full(len(y_test), y_test.mean())\n",
    "\n",
    "r2_score(y_test, y_test_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bd72e7d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2825cbb8",
   "metadata": {},
   "source": [
    "### Evaluating A Regression Model 2 - Mean Absolute Error (MAE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "03c87579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.136382352941176"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean absolute error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "y_preds = model.predict(X_test)\n",
    "mae = mean_absolute_error(y_test, y_preds)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b2fddd23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual values</th>\n",
       "      <th>predicted values</th>\n",
       "      <th>differences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>23.6</td>\n",
       "      <td>23.081</td>\n",
       "      <td>-0.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>32.4</td>\n",
       "      <td>30.574</td>\n",
       "      <td>-1.826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>13.6</td>\n",
       "      <td>16.759</td>\n",
       "      <td>3.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>22.8</td>\n",
       "      <td>23.460</td>\n",
       "      <td>0.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>16.1</td>\n",
       "      <td>16.893</td>\n",
       "      <td>0.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>17.9</td>\n",
       "      <td>13.159</td>\n",
       "      <td>-4.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>9.6</td>\n",
       "      <td>12.476</td>\n",
       "      <td>2.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>17.2</td>\n",
       "      <td>13.612</td>\n",
       "      <td>-3.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>22.5</td>\n",
       "      <td>20.205</td>\n",
       "      <td>-2.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>21.4</td>\n",
       "      <td>23.832</td>\n",
       "      <td>2.432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     actual values  predicted values  differences\n",
       "173           23.6            23.081       -0.519\n",
       "274           32.4            30.574       -1.826\n",
       "491           13.6            16.759        3.159\n",
       "72            22.8            23.460        0.660\n",
       "452           16.1            16.893        0.793\n",
       "..             ...               ...          ...\n",
       "412           17.9            13.159       -4.741\n",
       "436            9.6            12.476        2.876\n",
       "411           17.2            13.612       -3.588\n",
       "86            22.5            20.205       -2.295\n",
       "75            21.4            23.832        2.432\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data={\"actual values\": y_test,\n",
    "                        \"predicted values\": y_preds})\n",
    "\n",
    "df[\"differences\"] = df[\"predicted values\"] - df[\"actual values\"]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8fc98ba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw plot for above data\n",
    "fig, ax = plt.subplots()\n",
    "x = np.arange(0, len(df), 1)\n",
    "ax.scatter(x, df[\"actual values\"], c='b', label=\"Actual Values\")\n",
    "ax.scatter(x, df[\"predicted values\"], c='r', label='Predictions')\n",
    "ax.legend(loc=(1, 0.5));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2799927",
   "metadata": {},
   "source": [
    "### Evaluating A Regression Model 3 - Mean Squared Error (MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "202f1275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.867437068627442"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_preds = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_preds)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c6be7887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     0.269361\n",
       "274     3.334276\n",
       "491     9.979281\n",
       "72      0.435600\n",
       "452     0.628849\n",
       "         ...    \n",
       "412    22.477081\n",
       "436     8.271376\n",
       "411    12.873744\n",
       "86      5.267025\n",
       "75      5.914624\n",
       "Name: differences, Length: 102, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate MSE by handsquared = \n",
    "squared = np.square(df[\"differences\"])\n",
    "squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "78d13a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.867437068627439"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squared.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057bdddb",
   "metadata": {},
   "source": [
    "> MSE will always be higher than MAE because is squares the errors rather than only taking the absolute difference into account.\n",
    "\n",
    "**Now you might be thinking, which regression evaluation metric should you use?**\n",
    "\n",
    "* R^2 is similar to accuracy. It gives you a quick indication of how well your model might be doing. Generally, the closer your R^2 value is to 1.0, the better the model. But it doesn't really tell exactly how wrong your model is in terms of how far off each prediction is.\n",
    "\n",
    "* MAE gives a better indication of how far off each of your model's predictions are on average.\n",
    "\n",
    "* As for MAE or MSE, because of the way MSE is calculated, squaring the differences between predicted values and actual values, it amplifies larger differences. Let's say we're predicting the value of houses (which we are).\n",
    "\n",
    "> **Pay more attention to MAE:** When being `$10,000` off is *twice* as bad as being `$5,000` off.\n",
    "\n",
    "> **Pay more attention to MSE:** When being `$10,000` off is *more than twice* as bad as being `$5,000` off.\n",
    "\n",
    "***Note: What we've covered here is only a handful of potential metrics you can use to evaluate your models.***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b736db14",
   "metadata": {},
   "source": [
    "***Evaluation — What defines success? Is a 95% accurate machine learning model good enough?***\n",
    "\n",
    "You’ve defined your business problem in machine learning terms and you have data. Now define what defines success. There are different evaluation metrics for classification, regression and recommendation problems. Which one you choose will depend on your goal.\n",
    "\n",
    "> For this project to be successful, the model needs to be over 95% accurate at whether someone is at fault or not at fault.\n",
    "\n",
    "A 95% accurate model may sound pretty good for predicting who’s at fault in an insurance claim. But for predicting heart disease, you’ll likely want better results.\n",
    "\n",
    "Other things you should take into consideration for classification problems.\n",
    "\n",
    "* **False negatives** — Model predicts negative, actually positive. In some cases, like email spam prediction, false negatives aren’t too much to worry about. But if a self-driving cars computer vision system predicts no pedestrian when there was one, this is not good.\n",
    "\n",
    "* **False positives** — Model predicts positive, actually negative. Predicting someone has heart disease when they don’t, might seem okay. Better to be safe right? Not if it negatively affects the person’s lifestyle or sets them on a treatment plan they don’t need.\n",
    "\n",
    "* **True negatives** — Model predicts negative, actually negative. This is good.\n",
    "\n",
    "* **True positives** — Model predicts positive, actually positive. This is good.\n",
    "\n",
    "* **Precision** — What proportion of positive predictions were actually correct? A model that produces no false positives has a precision of 1.0.\n",
    "\n",
    "* **Recall** — What proportion of actual positives were predicted correctly? A model that produces no false negatives has a recall of 1.0.\n",
    "\n",
    "* **F1 score** — A combination of precision and recall. The closer to 1.0, the better.\n",
    "\n",
    "* **Receiver operating characteristic (ROC) curve & Area under the curve (AUC)** — The ROC curve is a plot comparing true positive and false positive rate. The AUC metric is the area under the ROC curve. A model whose predictions are 100% wrong has an AUC of 0.0, one whose predictions are 100% right has an AUC of 1.0.\n",
    "\n",
    "For regression problems (where you want to predict a number), you’ll want to minimise the difference between what your model predicts and what the actual value is. If you’re trying to predict the price a house will sell for, you’ll want your model to get as close as possible to the actual price. To do this, use MAE or RMSE.\n",
    "\n",
    "* **Mean absolute error (MAE)** — The average difference between your model's predictions and the actual numbers.\n",
    "\n",
    "* **Root mean square error (RMSE)** — The square root of the average of squared differences between your model's predictions and the actual numbers.\n",
    "\n",
    "Use RMSE if you want large errors to be more significant. Such as, \n",
    "\n",
    "* predicting a house to be sold at `$300,000` instead of `$200,000` and being off by `$100,000` is more than twice as bad as being off by `$50,000`. \n",
    "\n",
    "* Or MAE if being off by `$100,000` is twice as bad as being off by `$50,000`.\n",
    "\n",
    "Recommendation problems are harder to test in experimentation. One way to do so is to take a portion of your data and hide it away. When your model is built, use it to predict recommendations for the hidden data and see how it lines up.\n",
    "\n",
    "Let’s say you’re trying to recommend customers products on your online store. You have historical purchase data from 2010–2019. You could build a model on the 2010–2018 data and then use it to predict 2019 purchases. Then it becomes a classification problem because you’re trying to classify whether or not someone is likely to buy an item.\n",
    "\n",
    "However, traditional classification metrics aren’t the best for recommendation problems. Precision and recall have no concept of ordering.\n",
    "\n",
    "If your machine learning model returned back a list of 10 recommendations to be displayed to a customer on your website, you’d want the best ones to be displayed first right?\n",
    "\n",
    "* **Precision @ k (precision up to k)** — Same as regular precision, however, you choose the cutoff, k. For example, precision at `5`, means we only care about the top `5` recommendations. You may have `10,000` products. But you can’t recommend them all to your customers.\n",
    "\n",
    "> To begin with, you may not have an exact figure for each of these. But knowing what metrics you should be paying attention to gives you an idea of how to evaluate your machine learning project."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f018d9",
   "metadata": {},
   "source": [
    "### Evaluating A Model With Cross Validation and Scoring Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ede75909",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "clf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e784e27f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.83606557, 0.78333333, 0.78333333])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "cv_acc = cross_val_score(clf, X, y, cv=5, scoring=None)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "11616307",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated accuracy is: 82.48%\n"
     ]
    }
   ],
   "source": [
    "# cross-validated accuracy\n",
    "print(f'The cross-validated accuracy is: {np.mean(cv_acc)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "072a5e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated accuracy is: 82.48%\n"
     ]
    }
   ],
   "source": [
    "# Accuracy\n",
    "np.random.seed(42) # increase prediction rate\n",
    "cv_acc = cross_val_score(clf, X, y, cv=5, scoring=\"accuracy\")\n",
    "print(f'The cross-validated accuracy is: {np.mean(cv_acc)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "14a7bcdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8085601538512754"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision\n",
    "cv_precision = cross_val_score(clf, X, y, cv=5, scoring=\"precision\")\n",
    "np.mean(cv_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "b6c12c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8424242424242424"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recall\n",
    "cv_recall = cross_val_score(clf, X, y, cv=5, scoring=\"recall\")\n",
    "np.mean(cv_recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ca5d913d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.841476533416832"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F1\n",
    "cv_f1 = cross_val_score(clf, X, y, cv=5, scoring=\"f1\")\n",
    "np.mean(cv_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b441f88a",
   "metadata": {},
   "source": [
    "***How about our regression model?***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "ee34ee5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X = boston_df.drop(\"target\", axis=1)\n",
    "y = boston_df[\"target\"]\n",
    "\n",
    "model = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "34d35768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77231143, 0.86035935, 0.74664002, 0.47632078, 0.26630379])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "cv_r2 = cross_val_score(model, X, y, cv=5, scoring=None)\n",
    "cv_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "4be4bb25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6243870737930857"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check average differences\n",
    "np.mean(cv_r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "3eecd446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77231143, 0.86035935, 0.74664002, 0.47632078, 0.26630379])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "cv_r2 = cross_val_score(model, X, y, cv=5, scoring='r2')\n",
    "cv_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "406a3925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.13045098, -2.49771287, -3.45471287, -3.81509901, -3.11813861])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean absolute error\n",
    "# neg means: higher values are better than lower values\n",
    "cv_mae = cross_val_score(model, X, y, cv=5, scoring=\"neg_mean_absolute_error\")\n",
    "cv_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "353fd425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.003222869345758"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cv_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c92f10da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -7.8141513 , -12.94343325, -19.11614042, -46.28783248,\n",
       "       -19.48161818])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean squared error\n",
    "cv_mse = cross_val_score(model, X, y, cv=5, scoring='neg_mean_squared_error')\n",
    "cv_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "35ff2e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-21.12863512415064"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check differences average\n",
    "np.mean(cv_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b67b2c1e",
   "metadata": {},
   "source": [
    "### Evaluating A Model With Scikit-learn Functions\n",
    "\n",
    "**METRICS FUNCTIONS**\n",
    "\n",
    "***Classification evaluation functions***\n",
    "\n",
    "* Accuracy we can use `accuracy_score()`\n",
    "* Precision we can use `precision_score()`\n",
    "* Recall we can use `recall_score()`\n",
    "* F1 we can use `f1_score()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "48a67f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier metrics on the test set\n",
      "Accuracy: 85.25%\n",
      "Precision: 0.85\n",
      "Recall: 0.88\n",
      "F1: 0.86\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# use numpy random seed to learn better\n",
    "np.random.seed(42)\n",
    "\n",
    "# split data as per prediction column\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "# set how many percentage of data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# call model and fit to the data\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# make some predictions\n",
    "y_preds = clf.predict(X_test)\n",
    "\n",
    "# evaluate the classifier\n",
    "print(\"Classifier metrics on the test set\")\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_preds)*100:.2f}%\")\n",
    "print(f\"Precision: {precision_score(y_test, y_preds):.2f}\")\n",
    "print(f\"Recall: {recall_score(y_test, y_preds):.2f}\")\n",
    "print(f\"F1: {f1_score(y_test, y_preds):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d99b281",
   "metadata": {},
   "source": [
    "***Summary information for your classification model in above***\n",
    "* `Accuracy`: default **prediction/learning** rate.\n",
    "* `Precision`: how many **false positive** exist in model.\n",
    "* `Recall`: how many **false negative** exist in model.\n",
    "* `F1`: combination of **precision and recall**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19fd4d92",
   "metadata": {},
   "source": [
    "**Regression evaluation functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "3a5ffe44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression model metrics on the test set\n",
      "r^2: 86.54\n",
      "MAE: 2.14\n",
      "MSE: 9.87\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X = boston_df.drop(\"target\", axis=1)\n",
    "y = boston_df[\"target\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "model = RandomForestRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# make predictions using regression model\n",
    "y_preds = model.predict(X_test)\n",
    "\n",
    "# evaluate the regression model\n",
    "print(\"Regression model metrics on the test set\")\n",
    "print(f\"r^2: {r2_score(y_test, y_preds)*100:.2f}\")\n",
    "print(f\"MAE: {mean_absolute_error(y_test, y_preds):.2f}\")\n",
    "print(f\"MSE: {mean_squared_error(y_test, y_preds):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c18aeb",
   "metadata": {},
   "source": [
    "***Summary information for your regression model in above***\n",
    "* `r^2`: default **prediction/learning** rate.\n",
    "* `Mean Absolute Error (MAE)`: average differences between **Actual and Predicted values** in model.\n",
    "* `Mean Squared Error (MSE)`: squaring the differences between **Actual and Predicted values** in model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dcfb0ca",
   "metadata": {},
   "source": [
    "### Improving A Machine Learning Model\n",
    "\n",
    "**First predictions** = baseline predictions.\n",
    "**First model** = baseline model.\n",
    "\n",
    "**Way to Improve model**\n",
    "\n",
    "***From a data perspective:***\n",
    "* Could we collect more data? \"generally, the more data, the better\"\n",
    "* Could we improve our data? \n",
    "\n",
    "***From a model perspective***\n",
    "* Is there a better model we could use? \"chosing complex method to training model\"\n",
    "* Could we improve the current model? \"hyper-parameter\"\n",
    "\n",
    "***Hyperparameters vs Parameters***\n",
    "* **Parameters** = model find these patterns in data\n",
    "* **Hyperparameters** = settings on a model you can adjust to \"ptentially\" impove its ability to find patterns\n",
    "\n",
    "***three ways to adjust hyperparameters***\n",
    "* By hand\n",
    "* Randomly with RandomSearchCV\n",
    "* Exhaustively with GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4c44ec1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f7043a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be208ab",
   "metadata": {},
   "source": [
    "#### Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b645f880",
   "metadata": {},
   "source": [
    "![sklearn-hyperparameter-tuning-oven](documents/sklearn-hyperparameter-tuning-oven.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e1124f4",
   "metadata": {},
   "source": [
    "> ***Regarding above picture,*** with hyperparameter tuning we can figure out to increase temperature of bakery for best baked chicken."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f841c612",
   "metadata": {},
   "source": [
    "### Tuning Hyperparameters\n",
    "\n",
    "**By Hand**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd9256d3",
   "metadata": {},
   "source": [
    "![sklearn-train-valid-test-annotated](documents/sklearn-train-valid-test-annotated.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772dc5ac",
   "metadata": {},
   "source": [
    "**Split data for 3 sets, `Training`, `Validation` and `Test`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "7ca9e8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1630f93a",
   "metadata": {},
   "source": [
    "***best common options to pickup and adjust in hyperparameters***\n",
    "\n",
    "* `max_depth`: the maximum depth of the tree.\n",
    "* `max_features`: the number of features to consider when looking for the best split.\n",
    "* `min_samples_leaf` : the minimum number of samples required to be at a leaf node.\n",
    "* `min_samples_split`: min_samples_split specifies the minimum number of samples required to split an internal node, while min_samples_leaf specifies the minimum number of samples required to be at a leaf node.\n",
    "* `n_estimators` : the number of trees you want to build before taking the maximum voting or averages of predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "452160ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_preds(y_true, y_preds):\n",
    "    \"\"\"\n",
    "    Performs evaluation comparison on y_true labels vs. y_pred labels\n",
    "    in classification.\n",
    "    \"\"\"\n",
    "    accuracy = accuracy_score(y_true, y_preds)\n",
    "    precision = precision_score(y_true, y_preds)\n",
    "    recall = recall_score(y_true, y_preds)\n",
    "    f1 = f1_score(y_true, y_preds)\n",
    "    metric_dict = {\"accuracy\": round(accuracy, 2),\n",
    "                   \"precision\": round(precision, 2), \n",
    "                   \"recall\": round(recall, 2),\n",
    "                   \"f1\": round(f1, 2)}\n",
    "    print(f\"Acc: {accuracy * 100:.2f}%\")\n",
    "    print(f\"Precision: {precision:.2f}\")\n",
    "    print(f\"Recall: {recall:.2f}\")\n",
    "    print(f\"F1 score: {f1:.2f}\")\n",
    "\n",
    "    return metric_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "17ee3b68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 82.22%\n",
      "Precision: 0.81\n",
      "Recall: 0.88\n",
      "F1 score: 0.85\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.82, 'precision': 0.81, 'recall': 0.88, 'f1': 0.85}"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Shuffle the data\n",
    "# frac=1 -> 100% of data\n",
    "heart_disease_shuffled = heart_disease.sample(frac=1)\n",
    "\n",
    "# Split into X & y\n",
    "X = heart_disease_shuffled.drop(\"target\", axis=1)\n",
    "y = heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# Split the data into train, validation & test sets\n",
    "train_split = round(0.7 * len(heart_disease_shuffled)) # 70% of data\n",
    "valid_split = round(train_split + 0.15 * len(heart_disease_shuffled)) # 15% of data\n",
    "\n",
    "X_train, y_train = X[:train_split], y[:train_split]\n",
    "X_valid, y_valid = X[train_split:valid_split], y[train_split:valid_split]\n",
    "X_test, y_test = X[valid_split:], y[valid_split:]\n",
    "\n",
    "# show length of splited parts\n",
    "# len(X_train), len(X_valid), len(X_test)\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# make baseline predictions\n",
    "y_preds = clf.predict(X_valid)\n",
    "\n",
    "# evaluate the classifier on validation set\n",
    "baseline_metrics = evaluate_preds(y_valid, y_preds)\n",
    "baseline_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "1f07b02f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 84.44%\n",
      "Precision: 0.85\n",
      "Recall: 0.88\n",
      "F1 score: 0.86\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# create a second classifier with different hyperparameters\n",
    "clf_2 = RandomForestClassifier(n_estimators=250)\n",
    "clf_2.fit(X_train, y_train)\n",
    "\n",
    "# make predictions\n",
    "y_preds_2 = clf_2.predict(X_valid)\n",
    "\n",
    "# evaluate the 2nd classifier\n",
    "clf_2_metrics = evaluate_preds(y_valid, y_preds_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "52ab88c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 86.67%\n",
      "Precision: 0.91\n",
      "Recall: 0.84\n",
      "F1 score: 0.87\n"
     ]
    }
   ],
   "source": [
    "clf_3 = RandomForestClassifier(n_estimators=10,\n",
    "                               max_depth=30)\n",
    "clf_3.fit(X_train, y_train)\n",
    "y_preds_3 = clf_3.predict(X_valid)\n",
    "clf_3_metrics = evaluate_preds(y_valid, y_preds_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc66dc74",
   "metadata": {},
   "source": [
    "### Tuning Hyperparameters 2\n",
    "\n",
    "**Randomly with RandomSearchCV**\n",
    "\n",
    "> in cross-validation section, I said how can find best test data from all data, so this is the answer in below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5090aa2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=2; total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=1; total time=   0.0s\n",
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      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
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      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=1; total time=   0.0s\n",
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      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=24, min_samples_split=8, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=5; total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=5; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=3; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=12, min_samples_split=15, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=24, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=1; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=30, n_estimators=2; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=120, max_features=sqrt, min_samples_leaf=12, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=24, min_samples_split=30, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n",
      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=4; total time=   0.0s\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "grid = {\"n_estimators\": [1, 2, 3, 4, 5],\n",
    "        \"max_depth\": [None, 30, 60, 90, 120],\n",
    "        \"max_features\": [\"sqrt\"],\n",
    "        \"min_samples_split\": [6, 8, 15, 30],\n",
    "        \"min_samples_leaf\": [4, 12, 24]}\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# split into X & y\n",
    "X = heart_disease_shuffled.drop(\"target\", axis=1)\n",
    "y = heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Instatiate RandomForestClassifier\n",
    "# n_jobs -> how many cpu-core will use for operation.\n",
    "clf = RandomForestClassifier(n_jobs=1)\n",
    "\n",
    "# setup RandomizeSearchCV\n",
    "rs_clf = RandomizedSearchCV(estimator=clf,\n",
    "                            param_distributions=grid,\n",
    "                            n_iter=60, # number of models to try\n",
    "                            cv=5, # 5-fold cross-validation\n",
    "                            verbose=2) # print out results\n",
    "\n",
    "# Fit the RandomizedSearchCV version of clf\n",
    "rs_clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "1c0c1ea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 5,\n",
       " 'min_samples_split': 6,\n",
       " 'min_samples_leaf': 12,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 90}"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# found best hyperparameters using randomsearchCV\n",
    "rs_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "4ff86fad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 81.97%\n",
      "Precision: 0.79\n",
      "Recall: 0.82\n",
      "F1 score: 0.81\n"
     ]
    }
   ],
   "source": [
    "# make predictions with the best hyperparameters\n",
    "rs_y_preds = rs_clf.predict(X_test)\n",
    "\n",
    "# evaluate the predictions\n",
    "rs_metrics = evaluate_preds(y_test, rs_y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "fa427827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': 0.82, 'precision': 0.81, 'recall': 0.88, 'f1': 0.85} {'accuracy': 0.84, 'precision': 0.85, 'recall': 0.88, 'f1': 0.86} {'accuracy': 0.87, 'precision': 0.91, 'recall': 0.84, 'f1': 0.87}\n"
     ]
    }
   ],
   "source": [
    "# check previous models\n",
    "print(baseline_metrics, clf_2_metrics, clf_3_metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81dc45e7",
   "metadata": {},
   "source": [
    "### Tuning Hyperparameters 3\n",
    "\n",
    "**Exhaustively with GridSearchCV**\n",
    "> The main difference between `GridSearchCV` and `RandomizedSearchCV` is `GridSearchCV` searches across a grid of hyperparamters exhaustively, where as, `RandomizedSearchCV` searches across a grid of hyperparameters randomly (stopping after `n_iter` combinations).\n",
    "\n",
    "`n_estimators` higher number of estimators will spent more time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "fbb80b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another hyperparameter grid similar to rs_clf.best_params_\n",
    "grid_2 = {'n_estimators': [15],\n",
    "          'max_depth': [None, 30, 60, 90, 200, 500, 1200],\n",
    "          'max_features': ['sqrt'],\n",
    "          'min_samples_split': [2, 4, 6, 8],\n",
    "          'min_samples_leaf': [4, 8, 12, 16, 20]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f4b88a49",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 140 candidates, totalling 700 fits\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=12, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=4, n_estimators=15; total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=16, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=20, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=20, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=20, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=20, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=20, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
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      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
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     ]
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     "output_type": "stream",
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      "[CV] END max_depth=60, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
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      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=16, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
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      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=90, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
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      "[CV] END max_depth=200, max_features=sqrt, min_samples_leaf=8, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
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      "[CV] END max_depth=200, max_features=sqrt, min_samples_leaf=12, min_samples_split=4, n_estimators=15; total time=   0.0s\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
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      "[CV] END max_depth=200, max_features=sqrt, min_samples_leaf=12, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
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      "[CV] END max_depth=500, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
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      "[CV] END max_depth=500, max_features=sqrt, min_samples_leaf=8, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
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      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
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      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=15; total time=   0.0s\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
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     "output_type": "stream",
     "text": [
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=2, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=4, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=6, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=8, n_estimators=15; total time=   0.0s\n",
      "[CV] END max_depth=1200, max_features=sqrt, min_samples_leaf=20, min_samples_split=8, n_estimators=15; total time=   0.0s\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# split into X & y\n",
    "X = heart_disease_shuffled.drop(\"target\", axis=1)\n",
    "y = heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Instatiate RandomForestClassifier\n",
    "#  “n_jobs” to -1 when using a CPU and 1 when using a GPU\n",
    "clf = RandomForestClassifier(n_jobs=1)\n",
    "\n",
    "# setup RandomizeSearchCV\n",
    "gs_clf = GridSearchCV(estimator=clf,\n",
    "                      param_grid=grid_2,\n",
    "                      cv=5, # 5-fold cross-validation\n",
    "                      verbose=2) # print out results\n",
    "\n",
    "# Fit the RandomizedSearchCV version of clf\n",
    "gs_clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "acf4cf0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 90,\n",
       " 'max_features': 'sqrt',\n",
       " 'min_samples_leaf': 20,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 15}"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "83dbe8e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 83.61%\n",
      "Precision: 0.76\n",
      "Recall: 0.93\n",
      "F1 score: 0.84\n"
     ]
    }
   ],
   "source": [
    "gs_y_preds = gs_clf.predict(X_test)\n",
    "\n",
    "# evaluate the predictions\n",
    "gs_metrics = evaluate_preds(y_test, gs_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5ee523",
   "metadata": {},
   "source": [
    "***compare `baseline`, `byHand`, `randomsearchCV` and `gridsearchCV` metrics***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "541bdafb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_metrics = pd.DataFrame({\"baseline\": baseline_metrics,\n",
    "                                \"clf_2\": clf_2_metrics,\n",
    "                                \"clf_3\": clf_3_metrics,\n",
    "                                \"random search\": rs_metrics,\n",
    "                                \"grid search\": gs_metrics})\n",
    "\n",
    "compare_metrics.plot.bar(figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64292a06",
   "metadata": {},
   "source": [
    "### Quick Tip Correlation Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76916ae3",
   "metadata": {},
   "source": [
    "> In a hyperparameter tuning sense, there may be a better set we could find through more extensive searching with RandomizedSearchCV and GridSearchCV but it's likely these improvements will be marginal.\n",
    "\n",
    "***A few next ideas you could try:***\n",
    "\n",
    "* Collecting more data - Based on the results our models are getting now, it seems like they're finding some patterns. Collecting more data may improve a models ability to find patterns. However, your ability to do this will largely depend on the project you're working on.\n",
    "* Try a more advanced model - Although our tuned Random Forest model is doing pretty well, a more advanced ensemble method such as `XGBoost` or `CatBoost` might perform better."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb11f67",
   "metadata": {},
   "source": [
    "**CatBoost**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "275505d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6736525\ttotal: 13.8ms\tremaining: 606ms\n",
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      "40:\tlearn: 0.3467952\ttotal: 3.7s\tremaining: 361ms\n",
      "41:\tlearn: 0.3397848\ttotal: 3.7s\tremaining: 264ms\n",
      "42:\tlearn: 0.3336837\ttotal: 3.7s\tremaining: 172ms\n",
      "43:\tlearn: 0.3307579\ttotal: 3.89s\tremaining: 88.5ms\n",
      "44:\tlearn: 0.3274111\ttotal: 4.26s\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier, Pool\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# split into X & y\n",
    "X = heart_disease_shuffled.drop(\"target\", axis=1)\n",
    "y = heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# # Instatiate RandomForestClassifier\n",
    "# #  “n_jobs” to -1 when using a CPU and 1 when using a GPU\n",
    "# clf = RandomForestClassifier(n_jobs=1)\n",
    "\n",
    "# setup CatBoost\n",
    "catbost_clf = CatBoostClassifier(iterations=45,\n",
    "                           depth=16,\n",
    "                           learning_rate=0.1,\n",
    "                           loss_function='Logloss',\n",
    "                           verbose=True)\n",
    "\n",
    "# Fit the CatBoost version of clf\n",
    "catbost_clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "91f5929c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 88.52%\n",
      "Precision: 0.82\n",
      "Recall: 0.96\n",
      "F1 score: 0.89\n"
     ]
    }
   ],
   "source": [
    "catbost_y_preds = catbost_clf.predict(X_test)\n",
    "\n",
    "# evaluate the predictions\n",
    "catbost_metrics = evaluate_preds(y_test, catbost_y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "71955e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_metrics = pd.DataFrame({\"baseline\": baseline_metrics,\n",
    "                                \"clf_2\": clf_2_metrics,\n",
    "                                \"clf_3\": clf_3_metrics,\n",
    "                                \"random search\": rs_metrics,\n",
    "                                \"grid search\": gs_metrics,\n",
    "                                \"catboost\": catbost_metrics})\n",
    "\n",
    "compare_metrics.plot.bar(figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "680d046d",
   "metadata": {},
   "source": [
    "### Saving And Loading A Model\n",
    "\n",
    "**two ways to save and load machine learning models:**\n",
    "\n",
    "* with python's `pickle`module\n",
    "* with the `joblib`module\n",
    "\n",
    "***Pickle***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "9ee73079",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# save an existing model to file\n",
    "pickle.dump(catbost_clf, open(\"models/catboost_randomforest_classification_model_1.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "9b55c186",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load a saved model\n",
    "loaded_pickle_model = pickle.load(open(\"models/catboost_randomforest_classification_model_1.pkl\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "2cf2f1cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 88.52%\n",
      "Precision: 0.82\n",
      "Recall: 0.96\n",
      "F1 score: 0.89\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.89, 'precision': 0.82, 'recall': 0.96, 'f1': 0.89}"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make some predictions\n",
    "pickle_y_preds = loaded_pickle_model.predict(X_test)\n",
    "evaluate_preds(y_test, pickle_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18c2e4f",
   "metadata": {},
   "source": [
    "### Saving And Loading A Model 2\n",
    "\n",
    "***joblib***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "45748d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/catboost_randomforest_classification_model_1.joblib']"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "\n",
    "# save model to file\n",
    "dump(catbost_clf, filename=\"models/catboost_randomforest_classification_model_1.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "44c6576b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import a saved joblib model\n",
    "loaded_job_model = load(filename=\"models/catboost_randomforest_classification_model_1.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "257a398b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 88.52%\n",
      "Precision: 0.82\n",
      "Recall: 0.96\n",
      "F1 score: 0.89\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.89, 'precision': 0.82, 'recall': 0.96, 'f1': 0.89}"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make and evaluate joblib predictions\n",
    "joblib_y_preds = loaded_job_model.predict(X_test)\n",
    "evaluate_preds(y_test, joblib_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b290129",
   "metadata": {},
   "source": [
    "***According to Documentation, use `joblib`***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "490fee69",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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