Introduction to Machine Learning - 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation and Hyperparameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression, Lasso, Ridge, SGDRegressor\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.datasets import load_boston, load_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston = load_boston()\n",
    "boston.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_df = pd.DataFrame(boston['data'], columns=boston['feature_names'])\n",
    "boston_df['MEDV'] = boston['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>MEDV</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  MEDV  \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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = boston_df.corr()\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "sns.heatmap(cm, square=True, annot=True, ax=ax, cmap='RdBu');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(boston_df[['LSTAT', 'PTRATIO', 'RM', 'TAX', 'MEDV']]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set\n",
      "Train MSE :  22.391912761102265\n",
      "Train R2 Score:  0.7536265594928349\n",
      "Test Set\n",
      "Test MSE :  21.270474171128367\n",
      "Test R2 Score:  0.6896825282689119\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = boston_df.drop('MEDV', axis=1)\n",
    "y = boston_df['MEDV']\n",
    "\n",
    "# split the dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
    "\n",
    "# fit the model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# predict\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "#validation\n",
    "\n",
    "train_score = r2_score(y_train, y_train_pred)\n",
    "test_score = r2_score(y_test, y_test_pred)\n",
    "\n",
    "train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
    "\n",
    "print('Train Set')\n",
    "print('Train MSE : ', train_mse)\n",
    "print('Train R2 Score: ', train_score)\n",
    "print('Test Set')\n",
    "print('Test MSE : ', test_mse)\n",
    "print('Test R2 Score: ', test_score)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.bar(range(len(model.coef_)), height=model.coef_)\n",
    "ax.set_xticks(range(len(model.coef_)))\n",
    "ax.set_xticklabels(X_train.columns);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Folds Cross-Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='images/kfold.svg'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------Fold-------\n",
      "\n",
      "- Train Set\n",
      "Train MSE :  23.07658518008872\n",
      "Train R2 Score:  0.7209201855503373\n",
      "\n",
      "- Test Set\n",
      "Test MSE :  17.8423502543574\n",
      "Test R2 Score:  0.8045097824492671\n",
      "-------Fold-------\n",
      "\n",
      "- Train Set\n",
      "Train MSE :  19.62818862980289\n",
      "Train R2 Score:  0.7671093041513142\n",
      "\n",
      "- Test Set\n",
      "Test MSE :  32.655990795531544\n",
      "Test R2 Score:  0.6129413219010514\n",
      "-------Fold-------\n",
      "\n",
      "- Train Set\n",
      "Train MSE :  22.732634330958714\n",
      "Train R2 Score:  0.7244122085179627\n",
      "\n",
      "- Test Set\n",
      "Test MSE :  19.32228966284307\n",
      "Test R2 Score:  0.7869715237041519\n",
      "-------Fold-------\n",
      "\n",
      "- Train Set\n",
      "Train MSE :  21.353973031440756\n",
      "Train R2 Score:  0.7562764637393578\n",
      "\n",
      "- Test Set\n",
      "Test MSE :  26.519145162610535\n",
      "Test R2 Score:  0.6131838514915702\n",
      "-------Fold-------\n",
      "\n",
      "- Train Set\n",
      "Train MSE :  21.360749558452202\n",
      "Train R2 Score:  0.7479879309723831\n",
      "\n",
      "- Test Set\n",
      "Test MSE :  25.504552634787053\n",
      "Test R2 Score:  0.6924996095228477\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "kfold = KFold(n_splits=5, shuffle=True)\n",
    "\n",
    "for train, test in kfold.split(X, y):\n",
    "    print('-------Fold-------')\n",
    "    print()\n",
    "    model = LinearRegression()\n",
    "    model.fit(X.values[train], y.values[train])\n",
    "    \n",
    "    y_train_pred = model.predict(X.values[train])\n",
    "    y_test_pred = model.predict(X.values[test])\n",
    "\n",
    "    train_score = r2_score(y.values[train], y_train_pred)\n",
    "    test_score = r2_score(y.values[test], y_test_pred)\n",
    "\n",
    "    train_mse = mean_squared_error(y.values[train], y_train_pred)\n",
    "    test_mse = mean_squared_error(y.values[test], y_test_pred)\n",
    "\n",
    "    print('- Train Set')\n",
    "    print('Train MSE : ', train_mse)\n",
    "    print('Train R2 Score: ', train_score)\n",
    "    print('\\n- Test Set')\n",
    "    print('Test MSE : ', test_mse)\n",
    "    print('Test R2 Score: ', test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default cross_val_score does a Stratified K-Fold Cross Validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  [12.46030057 26.04862111 33.07413798 80.76237112 33.31360656]\n",
      "Mean:  37.13180746769911\n",
      "Std:  23.091945335664157\n",
      "----------------------------------------------------------------------\n",
      "Scores:  [ 0.63919994  0.71386698  0.58702344  0.07923081 -0.25294154]\n",
      "Mean:  0.35327592439587907\n",
      "Std:  0.3765678393326277\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "model = LinearRegression()\n",
    "scores = cross_val_score(model, X.values, y.values, cv=5, scoring='neg_mean_squared_error')\n",
    "r2_scores = cross_val_score(model, X.values, y.values, cv=5, scoring='r2')\n",
    "mse = -scores\n",
    "print('MSE: ', mse)\n",
    "print('Mean: ', mse.mean())\n",
    "print('Std: ', mse.std())\n",
    "print('-'*70)\n",
    "print('Scores: ', r2_scores)\n",
    "print('Mean: ', r2_scores.mean())\n",
    "print('Std: ', r2_scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  [36.1620778  19.73936007 19.53379665 16.96935584 28.52523594]\n",
      "Mean:  24.185965258923982\n",
      "Std:  7.151936292215164\n",
      "----------------------------------------------------------------------\n",
      "Scores:  [0.66984822 0.6362508  0.77860938 0.78596637 0.68646548]\n",
      "Mean:  0.7114280494517151\n",
      "Std:  0.06012141844093269\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "model = LinearRegression()\n",
    "scores = cross_val_score(model, X.values, y.values, cv=kfold, scoring='neg_mean_squared_error', )\n",
    "r2_scores = cross_val_score(model, X.values, y.values, cv=kfold, scoring='r2')\n",
    "mse = -scores\n",
    "print('MSE: ', mse)\n",
    "print('Mean: ', mse.mean())\n",
    "print('Std: ', mse.std())\n",
    "print('-'*70)\n",
    "print('Scores: ', r2_scores)\n",
    "print('Mean: ', r2_scores.mean())\n",
    "print('Std: ', r2_scores.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stratified K-Fold Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   class  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "iris_df = pd.DataFrame(iris['data'], columns=iris['feature_names'])\n",
    "iris_df['class'] = iris['target']\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(iris_df.shape)\n",
    "iris_df['class'].value_counts() / iris_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------Fold------------------------------\n",
      "Train Size: 135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    0.333333\n",
       "1    0.333333\n",
       "0    0.333333\n",
       "Name: class, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "skf = StratifiedKFold(n_splits=10)\n",
    "\n",
    "X = iris_df.drop('class', axis=1)\n",
    "y = iris_df['class']\n",
    "\n",
    "for train, test in skf.split(X, y):\n",
    "    print('-'*30 + 'Fold' + '-'*30)\n",
    "    \n",
    "    print('Train Size:', y[train].shape[0])\n",
    "    display(y[train].value_counts() / y[train].shape[0])\n",
    "    \n",
    "    print('Test Size:', y[test].shape[0])\n",
    "    display(y[test].value_counts() / y[test].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  [1.         0.93333333 1.         1.         0.93333333 0.93333333\n",
      " 0.93333333 1.         1.         1.        ]\n",
      "Mean:  0.9733333333333334\n",
      "Std:  0.03265986323710904\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(multi_class='multinomial', solver='newton-cg')\n",
    "\n",
    "scores = cross_val_score(clf, X, y, cv=skf, scoring='accuracy')\n",
    "acc = scores\n",
    "print('Accuracy: ', acc)\n",
    "print('Mean: ', acc.mean())\n",
    "print('Std: ', acc.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leave One Out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: LeaveOneOut() is equivalent to KFold(n_splits=n) and LeavePOut(p=1) where n is the number of samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  23.725745519476074\n",
      "Std:  65.26874647564769\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "\n",
    "X = boston_df.drop('MEDV', axis=1)\n",
    "y = boston_df['MEDV']\n",
    "loo = LeaveOneOut()\n",
    "\n",
    "model = LinearRegression()\n",
    "scores = cross_val_score(model, X.values, y.values, cv=loo, scoring='neg_mean_squared_error', )\n",
    "mse = -scores\n",
    "print('Mean: ', mse.mean())\n",
    "print('Std: ', mse.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shuffle Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  [20.93282101 14.48750964 16.10969516 21.20559921 34.27614939]\n",
      "Mean:  21.402354882854006\n",
      "Std:  6.954438749450195\n",
      "----------------------------------------------------------------------\n",
      "Scores:  [0.60829511 0.77664478 0.76637582 0.8032516  0.79640386]\n",
      "Mean:  0.7501942366806061\n",
      "Std:  0.07217652085400275\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "ss = ShuffleSplit(n_splits=5)\n",
    "\n",
    "scores = cross_val_score(model, X.values, y.values, cv=ss, scoring='neg_mean_squared_error', )\n",
    "r2_scores = cross_val_score(model, X.values, y.values, cv=ss, scoring='r2')\n",
    "mse = -scores\n",
    "print('MSE: ', mse)\n",
    "print('Mean: ', mse.mean())\n",
    "print('Std: ', mse.std())\n",
    "print('-'*70)\n",
    "print('Scores: ', r2_scores)\n",
    "print('Mean: ', r2_scores.mean())\n",
    "print('Std: ', r2_scores.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False),\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,\n",
       "       eta0=0.01, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='invscaling', loss='squared_loss', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, penalty='l2', power_t=0.25,\n",
       "       random_state=None, shuffle=True, tol=None, validation_fraction=0.1,\n",
       "       verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'eta0': [0.001, 0.01, 0.1, 0.5], 'fit_intercept': [True, False], 'max_iter': [100, 1000, 10000]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'eta0': [0.001, 0.01, 0.1, 0.5],\n",
    "               'fit_intercept': [True, False],\n",
    "               'max_iter': [100, 1000, 10000],\n",
    "               }]\n",
    "sgd_reg = SGDRegressor()\n",
    "grid_search = GridSearchCV(sgd_reg, param_grid, cv=KFold(n_splits=5),\n",
    "                          scoring='neg_mean_squared_error')\n",
    "\n",
    "grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eta0': 0.001, 'fit_intercept': True, 'max_iter': 10000}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'alpha': [0.01, 0.1, 0.5, 1, 10, 100]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "\n",
    "param_grid = [{'alpha': [0.01, 0.1, 0.5, 1, 10, 100]}]\n",
    "\n",
    "grid_search = GridSearchCV(ridge, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
    "\n",
    "grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 100}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.01874933, 0.01250091, 0.01252618, 0.00628071, 0.01562791,\n",
       "        0.00313406]),\n",
       " 'std_fit_time': array([0.02296415, 0.00625045, 0.00626335, 0.00769241, 0.00010245,\n",
       "        0.00626812]),\n",
       " 'mean_score_time': array([0.00625014, 0.003126  , 0.        , 0.00308542, 0.        ,\n",
       "        0.        ]),\n",
       " 'std_score_time': array([0.00765483, 0.006252  , 0.        , 0.00617085, 0.        ,\n",
       "        0.        ]),\n",
       " 'param_alpha': masked_array(data=[0.01, 0.1, 0.5, 1, 10, 100],\n",
       "              mask=[False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'alpha': 0.01},\n",
       "  {'alpha': 0.1},\n",
       "  {'alpha': 0.5},\n",
       "  {'alpha': 1},\n",
       "  {'alpha': 10},\n",
       "  {'alpha': 100}],\n",
       " 'split0_test_score': array([-12.44497288, -12.31966805, -11.95053799, -11.7110334 ,\n",
       "        -11.4217962 , -12.8247449 ]),\n",
       " 'split1_test_score': array([-25.94477456, -25.19800734, -23.87955061, -23.58316804,\n",
       "        -24.29446706, -27.27923137]),\n",
       " 'split2_test_score': array([-33.0042035 , -32.43466024, -30.7731107 , -29.69339407,\n",
       "        -28.14355069, -34.67968877]),\n",
       " 'split3_test_score': array([-80.75610865, -80.70282254, -80.49366416, -80.22988232,\n",
       "        -74.59916618, -52.69431476]),\n",
       " 'split4_test_score': array([-33.26561555, -32.88006851, -31.80168471, -31.11617767,\n",
       "        -28.51714218, -22.05052994]),\n",
       " 'mean_test_score': array([-37.03444301, -36.65884894, -35.73261641, -35.22017834,\n",
       "        -33.35179871, -29.87194512]),\n",
       " 'std_test_score': array([23.10996518, 23.22183171, 23.45138542, 23.5004917 , 21.51735509,\n",
       "        13.44296654]),\n",
       " 'rank_test_score': array([6, 5, 4, 3, 2, 1]),\n",
       " 'split0_train_score': array([-24.58930968, -24.59624118, -24.69790261, -24.86067663,\n",
       "        -25.82042517, -27.11137152]),\n",
       " 'split1_train_score': array([-22.24107773, -22.25342076, -22.38689537, -22.54109332,\n",
       "        -23.12018993, -24.15423302]),\n",
       " 'split2_train_score': array([-21.19062914, -21.20018928, -21.33563265, -21.54436591,\n",
       "        -22.71769884, -23.70931763]),\n",
       " 'split3_train_score': array([-12.91760423, -12.92112414, -12.97030992, -13.04512287,\n",
       "        -13.54554254, -16.83936829]),\n",
       " 'split4_train_score': array([-22.73723806, -22.74136594, -22.79630813, -22.87527934,\n",
       "        -23.29274073, -24.92675496]),\n",
       " 'mean_train_score': array([-20.73517177, -20.74246826, -20.83740974, -20.97330761,\n",
       "        -21.69931944, -23.34820908]),\n",
       " 'std_train_score': array([4.0608424 , 4.06234089, 4.08120275, 4.1077806 , 4.22048696,\n",
       "        3.45827359])}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\secan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'C': [1000, 100, 10, 5, 1]}], pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score='warn', scoring='accuracy',\n",
       "       verbose=0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "param_grid = [{'C': [1000, 100, 10, 5, 1]}]\n",
    "\n",
    "grid_search = GridSearchCV(clf, param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "X = iris_df.drop('class', axis=1)\n",
    "y = iris_df['class']\n",
    "grid_search.fit(X, y)"
   ]
  }
 ],
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