day-5-machine-learning-part-II/01 - Linear Models for Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Models for Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "Load and preprocess the adult data as before.\n",
    "include dummy encoding and scaling\n",
    "Learn a logistic regression model and visualize the coefficients.\n",
    "Then grid-search the regularization parameter C.\n",
    "compare L1 penalty to L2 penalty. how are the coefficients different?\n",
    "which are the most important features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:48:51.157962Z",
     "start_time": "2019-07-04T11:48:51.153133Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:45:58.978483Z",
     "start_time": "2019-07-04T11:45:57.381723Z"
    }
   },
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education  education-num       marital-status  \\\n",
       "0   39          State-gov   Bachelors             13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad              9             Divorced   \n",
       "3   53            Private        11th              7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race   gender  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "1     Exec-managerial         Husband   White     Male             0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "3   Handlers-cleaners         Husband   Black     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  income  \n",
       "0             0              40   United-States   <=50K  \n",
       "1             0              13   United-States   <=50K  \n",
       "2             0              40   United-States   <=50K  \n",
       "3             0              40   United-States   <=50K  \n",
       "4             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"data/adult.csv\", index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:46:27.328119Z",
     "start_time": "2019-07-04T11:46:27.305302Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
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       "      <th>hours-per-week</th>\n",
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       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
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       "      <td>United-States</td>\n",
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       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
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       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education  education-num       marital-status  \\\n",
       "0   39          State-gov   Bachelors             13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad              9             Divorced   \n",
       "3   53            Private        11th              7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race   gender  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "1     Exec-managerial         Husband   White     Male             0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "3   Handlers-cleaners         Husband   Black     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  \n",
       "0             0              40   United-States  \n",
       "1             0              13   United-States  \n",
       "2             0              40   United-States  \n",
       "3             0              40   United-States  \n",
       "4             0              40            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income = data.income\n",
    "data_features = data.drop(\"income\", axis=1)\n",
    "display(data_features.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:46:40.187127Z",
     "start_time": "2019-07-04T11:46:40.111871Z"
    }
   },
   "outputs": [
    {
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       "</table>\n",
       "<p>5 rows × 107 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "0   39             13          2174             0              40   \n",
       "1   50             13             0             0              13   \n",
       "2   38              9             0             0              40   \n",
       "3   53              7             0             0              40   \n",
       "4   28             13             0             0              40   \n",
       "\n",
       "   workclass_ ?  workclass_ Federal-gov  workclass_ Local-gov  \\\n",
       "0             0                       0                     0   \n",
       "1             0                       0                     0   \n",
       "2             0                       0                     0   \n",
       "3             0                       0                     0   \n",
       "4             0                       0                     0   \n",
       "\n",
       "   workclass_ Never-worked  workclass_ Private  ...  native-country_ Portugal  \\\n",
       "0                        0                   0  ...                         0   \n",
       "1                        0                   0  ...                         0   \n",
       "2                        0                   1  ...                         0   \n",
       "3                        0                   1  ...                         0   \n",
       "4                        0                   1  ...                         0   \n",
       "\n",
       "   native-country_ Puerto-Rico  native-country_ Scotland  \\\n",
       "0                            0                         0   \n",
       "1                            0                         0   \n",
       "2                            0                         0   \n",
       "3                            0                         0   \n",
       "4                            0                         0   \n",
       "\n",
       "   native-country_ South  native-country_ Taiwan  native-country_ Thailand  \\\n",
       "0                      0                       0                         0   \n",
       "1                      0                       0                         0   \n",
       "2                      0                       0                         0   \n",
       "3                      0                       0                         0   \n",
       "4                      0                       0                         0   \n",
       "\n",
       "   native-country_ Trinadad&Tobago  native-country_ United-States  \\\n",
       "0                                0                              1   \n",
       "1                                0                              1   \n",
       "2                                0                              1   \n",
       "3                                0                              1   \n",
       "4                                0                              0   \n",
       "\n",
       "   native-country_ Vietnam  native-country_ Yugoslavia  \n",
       "0                        0                           0  \n",
       "1                        0                           0  \n",
       "2                        0                           0  \n",
       "3                        0                           0  \n",
       "4                        0                           0  \n",
       "\n",
       "[5 rows x 107 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### one hot encode data\n",
    "data_one_hot = pd.get_dummies(data_features)\n",
    "data_one_hot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:47:08.114829Z",
     "start_time": "2019-07-04T11:47:06.019690Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lgaalves/miniconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/Users/lgaalves/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:7: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "### Preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_one_hot, income)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:47:32.729071Z",
     "start_time": "2019-07-04T11:47:27.234220Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lgaalves/miniconda3/lib/python3.6/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",
      "/Users/lgaalves/miniconda3/lib/python3.6/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",
      "/Users/lgaalves/miniconda3/lib/python3.6/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",
      "/Users/lgaalves/miniconda3/lib/python3.6/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",
      "/Users/lgaalves/miniconda3/lib/python3.6/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8475429975429976\n"
     ]
    }
   ],
   "source": [
    "### Cross-validation with default parameters\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "scores = cross_val_score(LogisticRegression(), X_train_scaled, y_train, cv=5)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:50:12.835561Z",
     "start_time": "2019-07-04T11:49:37.672634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'C': 0.1}, 0.8478296478296479)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### do grid search\n",
    "import numpy as np\n",
    "\n",
    "param_grid = {'C': np.logspace(-3, 3, 7)}\n",
    "param_grid\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "grid = GridSearchCV(LogisticRegression(), param_grid, cv=5)\n",
    "\n",
    "grid.fit(X_train_scaled, y_train)\n",
    "\n",
    "grid.best_params_,grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:52:41.836257Z",
     "start_time": "2019-07-04T11:52:41.829606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:52:23.180935Z",
     "start_time": "2019-07-04T11:52:22.993847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Score')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# some visualization\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "res = pd.DataFrame(grid.cv_results_)\n",
    "res.mean_test_score.plot()\n",
    "res.mean_train_score.plot()\n",
    "plt.xlabel('Parameter, $C$',fontsize=20)\n",
    "plt.ylabel('Score',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:50:29.575751Z",
     "start_time": "2019-07-04T11:50:29.000295Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xEVGffraJiKcj4kXgH5IGAkPIZhearJ/2fXB+Jf0gnZ+5ub7y71f5e7mOpO2A4WSPdh8IjKX596/S+/kx4J+58dVHxKdz+50kmJl1ArVOKKrRC3gxbQ+tss1yYKPWxIiIw9LF6qz0qbhXRPweOI9Vjysvj7sR8DdJ6wInN9P/EmCPtF1aOIqkTwEvRMR1ZNP3A6s8vtKY/wVck8ZYMg74mtLHeUm75fbdCVyQjm1BFfXzfV1YuqC3YogbkyUJyyRtARzeiralfl8HFks6IY1PknZNuycDX0rbJ1dqb2ZmHWNNSCiuBH4oaTLZffhqTAD6lRZlrkaMjYCHJM0HHidbfwDZBflbafHi9sBFwHSyWwnP5NqX17sa+KqkKWRrKEqGAAvTp/6dgdX5VsMv4EO3ri4ju6UzX9LC9LrkbrIL8G+rrF9IRMwD5gCLyNbHTF7NUCcDZ0qal2Idk8q/DvyXpJlkSaOZmdVIadGh2VqvoaEhGhsbaz0MM7M1iqRZEdHQUr01YYbCzMzMOrlaf8vDciRtRvYVyXKfjYh/dPR4zMzMquWEohNJSUNrFj2amZl1Cr7lYWZmZoU5oTAzM7PCnFCYmZlZYV5DYV3GgheXUTdibK2HscZbcsWRtR6CmXVCnqEwMzOzwpxQmJmZWWFOKMzMzKwwJxSdmKS69HwNq0DSEkm9W65pZmbtzQlFFyOpQxbiSqr2QW5mZrYWcELR+XWTdJOkRZIekbS+pHpJ0yTNl3SfpI8DSJooqSFt95a0JG0PlXSXpAeBRyT1kTQpPY11oaT9yztNbX4n6WFJz0r6Xm7fKZJmpPY3lpIHSW9IulTSdGDvsng3SDo6bd8n6Za0faak77cQ91BJUyXNTsexYVns9dM4z26jc25mZq3khKLz6wv8NCL6A/8EjiN7zPn/i4iBwALge820L9kbOD0iDga+DIyLiHpgV2BuE20GkT06vB44QVKDpE+TPXZ939R+ZaoDsAGwMCL2jIgny2JNAkqJy1ZAv7S9H/BEU3HTLY3vAodExO5AI3B+Lu6GwIPAbyLipirOg5mZtQP/HYrOb3FElC74s4DtgU0i4vFUditwVxVxHo2IV9P2TOAWSesC9+fiV2rzDwBJ95Jd/N8D9gBmSgJYH3g51V8J3NNErCeA8yT1A54CPi6pD1micy5wehNx9yJLPian8u7A1Fzc3wFXRsTtlTqVNAwYBtBt482bGJqZmRXlhKLzW5HbXgls0kzd91g169SjbN+bpY2ImCTpAOBI4DZJVwHLWTXTcVapalmMAATcGhHfrtD/OxGxEkDSnsCNqfziiHgg3Zr5PNlsxabAicAbEbFcWbbwkbiSjiJLbE5q4pgnA4dL+k1ElI+XiBgFjAJYr0/fj+w3M7O24Vsea55lwGu5dQ+nAqXZiiVkn/IBjm8qgKRtgZfTLYJfALtHxH0RUZ9+GlPVz0naVNL6wLFkF+/xwPGSPpFibZrifUhETM/FeyAVTwXOI0songCGp980E3casK+kHVJ5T0k75rq6GPgHcENzJ83MzNqXE4o10+nAVZLmk61vuDSVXw18VdIUoLmvUw4G5kqaQ7Ym48dN1HsSuI1sjcU9EdEYEU+RrWl4JPX/KNCnynE/AawTEX8EZpPNUjwB0FTciFgKDAXuSOXTgJ3L4p4H9JB0ZZXjMDOzNqYKs8RmSBoKNETEObUeS1tZr0/f6HP6tbUexhrPz/Iw61okzYqIhpbqeYbCzMzMCvOiTKsoIkYDo2s8DDMzW0N4hsLMzMwK8wyFdRkDtupFo+//m5m1C89QmJmZWWFOKMzMzKwwJxRmZmZWmNdQWJex4MVl1I0YW+thrNH8NyjMrCmeoTAzM7PCnFCYmZlZYU4ozMzMrDAnFGZmZlaYEwozMzMrzAmFdRqS7pc0S9IiScNS2ZmSnpM0UdJNkq5P5ZtLukfSzPSzb21Hb2bWtflro9aZ/HtEvCppfWCmpLHARcDuwHLgMWBeqvtj4JqIeFLSNsA44NO1GLSZmTmhsM7lXElfSNtbA6cCj0fEqwCS7gJ2TPsPAfpJKrXdWNJGEbE8HzDNdAwD6Lbx5u08fDOzrssJhXUKkgaTJQl7R8RbkiYCz9L0rMPHUt23m4sbEaOAUQDr9ekbbTZgMzP7EK+hsM6iF/BaSiZ2BvYCegIHSvq4pHWA43L1HwHOKb2QVN+hozUzsw9xQmGdxcPAOpLmA5cB04AXgcuB6cAfgKeAZan+uUCDpPmSngK+0vFDNjOzEt/ysE4hIlYAh5eXS2qMiFFphuI+spkJIuIVYEjHjtLMzJriGQrr7EZKmgssBBYD99d4PGZmVoFnKKxTi4jhtR6DmZm1zDMUZmZmVphnKKzLGLBVLxqvOLLWwzAzWyt5hsLMzMwKc0JhZmZmhTmhMDMzs8K8hsK6jAUvLqNuxNhaD6NTW+I1Jma2mjxDYWZmZoU5oTAzM7PCnFCYmZlZYU4ozMzMrDAnFGsJSUMlXd/GMY+V1C/3+lJJh7RlH2ZmtnZwQmHNORb4IKGIiIsj4g81HI+ZmXVSTijWEJJOkTRD0lxJN0rqJukMSc9JehzYN1d3tKTjc6/fyG1fIGmBpHmSrkhlZ0uamcrukdRT0j7A0cBVqc/t83ElfVbSnBTrFknrpfIlki6RNDvt27mJ46lYT9JIScNz9RZKqks/z0i6OZXdLukQSZMlPS9pUJuecDMzaxUnFGsASZ8GhgD7RkQ9sBI4BbiELJH4HLmZhGbiHE4267BnROwKXJl23RsRn0llTwNnRsQU4AHgWxFRHxF/ysXpAYwGhkTEALK/Z/LVXFevRMTuwM+A5p4WWm29kh2AHwMDgZ2BLwP7pbbfaeKYh0lqlNS48q1lVXRhZmarwwnFmuGzwB7ATElz0+tvABMjYmlEvAuMqSLOIcAvI+ItgIh4NZXvIukJSQuAk4H+LcTZCVgcEc+l17cCB+T235t+zwLqmolTbb2SxRGxICLeBxYB4yMigAVNtY+IURHREBEN3Xr2qqILMzNbHU4o1gwCbk0zBfURsRMwEogm6r9Hem8lCeiei1OpzWjgnDTbcAnQo4rxNGdF+r2S9NdYJY1Lt05ubq5efuxJjwr1Ad7PvX4f/9VXM7OackKxZhgPHC/pEwCSNgXmAIMlbSZpXeCEXP0lZDMaAMcA66btR4B/l9QzFwdgI+BvKc7JuTjL075yzwB1knZIr08FHm/uACLisJQMndXCsS4Bdk/j2x3YroX6ZmbWCTihWANExFPAd4FHJM0HHgX6kM1STAX+AMzONbkJOFDSDGBP4M0U52GydRGN6dZJad3CRcD0FPeZXJw7gW+lxZfb58bzDnAGcFe6TfI+8PM2Otx7gE3T+L4KPNdCfTMz6wSU3YI2W/ut16dv9Dn92loPo1Pzw8HMrJykWRHR0FI9z1CYmZlZYU4ozMzMrDCvjLcuY8BWvWj0lL6ZWbvwDIWZmZkV5oTCzMzMCnNCYWZmZoV5DYV1GQteXEbdiLG1HkYh/lqnmXVWnqEwMzOzwpxQmJmZWWFOKMzMzKwwJxRmZmZWmBMKMzMzK8wJRSclaaKkZh/GIum80qPI0+vfS9qkDccwUtLwJvZNKRj7GEn3515/W9Ifc6+PkvRA2v7guCSdK+lpSbcX6d/MzNqWE4oaUabo+T8P+CChiIgjIuKfBWNWJSL2KRhiCrB37vXewOuSPpFe7wNMTn3lj+s/gSMi4uSC/ZuZWRtyQtGBJNWlT9c3ALOBrSUdKmmqpNmS7pK0YYV2P5PUKGmRpEtS2bnAlsAESRNS2RJJvdP2+ZIWpp/zyvq/KcV6RNL6pXiSnpI0X9Kdue77pdmSF1KfpTG9kX4PljRJ0n2p/c+rSZQiYimwTNIOqWgr4B6yRIL0e0r+uCT9HPgU8ICkb0jaQNItkmZKmiPpmKreCDMza3NOKDreTsCvImI34E3gu8AhEbE70AicX6HNhelZ9AOBAyUNjIjrgJeAgyLioHxlSXsAZwB7AnsBZ0vaLe3uC/w0IvoD/wSOS+UjgN0iYiDwlVy4nYHDgEHA9yStW2F8g4BvAgOA7YEvVnkupgD7SNoJeB6Yll6vk451Zr5yRHwld8zXABcCj0XEZ4CDgKskbVB2LoalZKxx5VvLqhyWmZm1lhOKjvfniJiWtvcC+gGTJc0FTge2rdDmREmzgTlA/9SmOfsB90XEmxHxBnAvsH/atzgi5qbtWUBd2p4P3C7pFOC9XKyxEbEiIl4BXga2qNDfjIh4ISJWAnek/qsxmWwmYh9gKjCDLAnaDXg2It5pof2hwIh07iYCPYBt8hUiYlRENEREQ7eevaoclpmZtZb/9HbHezO3LeDRiDipqcqStgOGA5+JiNckjSa7cDZHzexbkdteCayfto8EDgCOBi6S1L+J+pX+zUQLr5syBfga0A24KSKWS+oBDCatn2iBgOMi4tkq+zMzs3biGYramgbsW1pHIKmnpB3L6mxMloQsk7QFcHhu33JgowpxJwHHpngbAF8AnmhqEGnNw9YRMQG4ANgE+MhajmYMkrRdijMEeLLKdk+RrQPZn2z2BWAu2S2Xar5FMg74miQB5G7rmJlZB3NCUUNpYeJQ4A5J88kSjJ3L6swju9guAm7hw5/cRwH/W1qUmWszGxhNdgthOnBzRMyhad2AX0takPq6ppXfFpkKXAEsBBYD91XTKCIije+ViPhXLtanqC6huAxYF5gvaWF6bWZmNaDs/+lmq0fSYGB4RPxbrcfSkvX69I0+p19b62EU4qeNmllHkzQrfTGgWZ6hMDMzs8K8KNMKiYiJZN+w+BBJ04H1yopPjYgFHTAsMzPrYE4orF1ExJ61HkO5AVv1otG3DMzM2oVveZiZmVlhTijMzMysMCcUZmZmVpgTCjMzMyvMCYWZmZkV5oTCzMzMCnNCYWZmZoU5oTAzM7PCnFBUSdJESc3+LXNJ50nqmXv9e0mbtOEYRkoa3sS+ah6m1VL87pKulfQnSc9L+p2kTxaNW2XfLR6bpLr0ELBKdVp8f8zMrP04oUiUKXo+zgM+SCgi4ohWPrVztUXEPm0Q5nKyx6HvGBF9gfuBe0uPB6+VNjo2MzNrR106oUifeJ+WdAMwG9ha0qGSpkqaLekuSRtWaPczSY2SFkm6JJWdC2wJTCg9TlzSEkm90/b5khamn/PK+r8pxXpE0vqleJKekjRf0p257vulT+MvpD5LY3oj/R4saZKk+1L7n1eTKKWZlTOAb0TESoCI+CWwAjhY0gWl/iRdI+mxtP1ZSb8ujUHSDyTNkzRN0hZN9PX5dH7nSRpf7bGVxVhf0p3p/IwB1m/pGM3MrP106YQi2Qn4VUTsBrwJfBc4JCJ2BxqB8yu0uTA9ynUgcKCkgRFxHfAScFBEHJSvLGkPsov1nsBewNmSdku7+wI/jYj+wD+B41L5CGC3iBgIfCUXbmfgMGAQ8D1J61YY3yDgm8AAYHvgi1Wchx2A/4uI18vKG4H+wCRg/1TWAGyY+t4PeCKVbwBMi4hdU/2zyzuRtDlwE3BcqndCK4+t5KvAW+n8/ADYo1IlScNS8te4dOnSZsKZmVkRTijgzxExLW3vBfQDJkuaC5wObFuhzYmSZgNzyC62/VroYz/gvoh4MyLeAO5l1cV5cUTMTduzgLq0PR+4XdIpwHu5WGMjYkVEvAK8DFSaBZgRES+kmYY7Uv8tERDNlM8C9pC0EdmsxVSyxGJ/ViUU7wIPVTiWvL2ASRGxGCAiXm3lsZUcAPw6xZhPdr4+IiJGRURDRDRsvvnmzYQzM7Mi/LTRbFaiRMCjEXFSU5UlbQcMBz4TEa9JGg30aKGP5tYgrMhtr2TV1P2RZBfNo4GLJPVvon6l97A8MaiUKJT7I7CtpI0iYnmufHfgwYj4l6QlZDMtU8gu4AeRzYA8ner+KyJKfa0E1pHUjSy5AHiAbMajqfFUc2x51RyXmZl1AM9QfNg0YF9JO0C2rkDSjmV1NiZLQpalNQKH5/YtJ1vUWG4ScGyKtwHwBVZ9qv+ItOZh64iYAFwAbAJ8ZC1HMwZJ2i7FGQI82VKDiHgTuBX4n5QEIOk0skWmj+WOY3j6/QTZrZi5uSSiUtyVEVGffi4mm9k4MCWY6FO0AAALsElEQVRmSNq0FceVNwk4OcXYhez2k5mZ1YgTipyIWAoMBe6QNJ8swdi5rM48slsdi4BbgMm53aOA/y0tysy1mQ2MBmYA04GbI2JOM0PpBvxa0oLU1zWt/LbIVOAKYCGwGLivynbfBt4BnpP0PNn6hi/kEoYngD7A1Ij4e6rbZGJUSTrHw8i+PTIPGNOa9jk/I1vHMZ8s6ZqxmnHMzKwNqJkPl7YGkjQYGB4R/1brsXQ2DQ0N0djYWOthmJmtUSTNSl9EaJZnKMzMzKwwL8pcy0TERGBiebmk6cB6ZcWnRsSCDhiWmZmt5ZxQdBERsWetx2BmZmsv3/IwMzOzwpxQmJmZWWFOKMzMzKwwJxRmZmZWmBMKMzMzK8wJhZmZmRXmhMLMzMwKc0JhZmZmhTmhaCeS6iUdkXt9tKQR7djfJyX9TtLzkv4k6ceSujcxlpGShrfXWNqKpJsl9av1OMzMrGVOKNpPPfDBRTwiHoiIK9qjI0kC7gXuj4i+wI5kjzv/QaWxtEF/3ToiVkScFRFPtVVfZmbWftaqhELS+ZIWpp/zcuWnSZovaZ6k21LZFpLuS2XzJO0jqU7Swly74ZJGpu2Jkq6VNCXFH5TKB6WyOen3Tmlm4FJgiKS5koZIGirp+tRmW0nj05jGS9omlY+WdF2K84Kk46s89IOBdyLilwARsRL4BvDvkjYuH0tq0y8d0wuSzs0d8ymSZqS6N5Yu+JLekHRpeibI3mXnvY+kSanNQkn7p/JDJU2VNFvSXZI2TOVLJF0s6UngAkkzcrHq0iPJS+e8IW1/PsWZJ2l8KttA0i2SZqbzf0yV58vMzNrYWpNQSNoDOAPYE9gLOFvSbpL6AxcCB0fErsDXU5PrgMdT2e7Aoiq62SAi9gH+E7gllT0DHBARuwEXA5dHxLtpe0xE1EfEmLI41wO/ioiBwO1pLCV9gP2AfwOqndHoD8zKF0TE68D/AXVNjGVn4DBgEPA9SetK+jQwBNg3IuqBlcDJpWMHFkbEnhHxZFn/XwbGpTa7AnMl9Qa+CxwSEbsDjcD5uTbvRMR+EfFDoLukT6XyIcBv88ElbQ7cBByX3q8T0q4Lgcci4jPAQcBVkjYoaztMUqOkxqVLlzZ7Es3MbPWtTQ8H2w+4LyLeBJB0L7A/EMDdEfEKQES8muofDJyWylYCyyR9vIU+7kj1J0naWNImwEbArZL6pr7WrWKsewNfTNu3AVfm9t0fEe8DT0naoopYAEp9V1sOMDYiVgArJL0MbAF8FtgDmJndRWF94OVUfyVwTxOxZgK3SFo3jX+upAOBfsDkFKs7MDXXJp9k/RY4kSyBGpJ+8vYCJkXEYvjQe3gocHRuPUgPYBvg6VLDiBgFjAJoaGho6lyYmVlBa1NCoWbKq72QvMeHZ216lO0vjxPAZcCEiPiCpDoqPDq8Cvm4K3LbTR1TuUXAcfmCdKtja+BPZElCuXw/K8n+LQi4NSK+XaH+OynxQtKewI2p/OKIeEDSAcCRwG2SrgJeAx6NiJOaGPObue0xwF0pCYyIeL6sbnMJ03ER8WwTfZiZWQdZa255AJOAYyX1TNPeXwCeAMYDJ0raDEDSpqn+eOCrqaxbugD/HfiEpM0krUd22yFvSKq/H7AsIpYBvYAX0/6hubrLyWYvKpkCfCltnwyU30JorfFAT0mnpfF1A/4bGB0Rb7UwlvI4x0v6RIqzqaRtyytFxPR0+6Q+JRPbAi9HxE3AL8huIU0D9pW0Q4rVU9KOlTqNiD+RJTUX8eGZi5KpwIGStiuNK5WPA76mNAUiabcqjtHMzNrBWpNQRMRsYDQwA5gO3BwRcyJiEdm3HR6XNA/4n9Tk68BBkhaQrT/oHxH/IlvAOB14iGx9RN5rkqYAPwfOTGVXAj+UNBnIf2NhAtnCx/xCyJJzgTPS4sNTWbWuY3WPPcgSqBMkPQ88B7wDfKeKseTjPEW27uGRNLZHydZ0tGQw2bqJOWQzJT+OiKVkCdYdKdY0snUbTRkDnELZ+ok0rqXAMODe9B6Wko7LyG4xzVe2mPayKsZqZmbtQNm1yFoiaSIwPCIaaz0WWz0NDQ3R2Oi3z8ysNSTNioiGluqtNTMUZmZmVjtr06LMdhURg2vRr6QBZN8EyVsREXvWYjxmZmaVOKHo5CJiAdlfujQzM+u0fMvDzMzMCnNCYWZmZoU5oTAzM7PCvIbCuowFLy6jbsTYDu93yRVHdnifZmYdzTMUZmZmVpgTCjMzMyvMCYWZmZkV5oTCWk3SlpLuTtv1ko6oos1gSQ81sW+ipBb/rKuZmXVeTiis1SLipYg4Pr2sB1pMKMzMbO3mhKILknSapPmS5km6TdJRkqZLmiPpD5K2SPVGpv2PSXpe0tmpvE7SQkndyZ7OOqT0JFNJgyRNSbGmSNqplWM7SdKCFP9HqaybpNGpbIGkb6TycyU9lY7lzrY9S2Zm1hr+2mgXI6k/cCGwb0S8ImlTIIC9IiIknQVcAHwzNRkI7AVsAMyR9MH3LiPiXUkXAw0RcU6KvzFwQES8J+kQ4HKyR5pXM7YtgR8BewCvkT1G/VjgL8BWEbFLqrdJajIC2C4iVuTKzMysBpxQdD0HA3dHxCsAEfFqegDZGEl9gO7A4lz930XE28DbkiYAg4C5zcTvBdwqqS9ZorJuK8b2GWBiRCwFkHQ7cABwGfApST8BxgKPpPrzgdsl3Q/cXymgpGHAMIBuG2/eiqGYmVlr+JZH1yOyC33eT4DrI2IA8B9Aj9y+8rrlr8tdBkxIswlHlcXKBiCNS7dIbq4wto+IiNeAXYGJwH8BpXZHAj8lm9GYJekjCXJEjIqIhoho6NazVwtDNzOz1eWEousZD5woaTOAdMujF/Bi2n96Wf1jJPVI9QcDM8v2Lwc2yr3OxxpaaQARcVhE1EfEWWW7pgMHSuotqRtwEvC4pN7AxyLiHuAiYHdJHwO2jogJZLdoNgE2bPHozcysXfiWRxcTEYsk/YDsQr0SmAOMBO6S9CIwDdgu12QG2W2GbYDLIuIlSXW5/ROAEZLmAj8EriS75XE+8Fgrx/Y3Sd9OMQX8PiJ+J2lX4JcpiQD4NtAN+LWkXqnuNRHxz9b0Z2ZmbUcRLc1gW1claSTwRkRcXeuxtIX1+vSNPqdf2+H9+lkeZrYmkzQrIlr8W0G+5WFmZmaF+ZaHNSkiRtZ6DGZmtmbwDIWZmZkV5hkK6zIGbNWLRq9nMDNrF56hMDMzs8KcUJiZmVlhTijMzMysMCcUZmZmVpgTCjMzMyvMCYWZmZkV5oTCzMzMCnNCYWZmZoU5oTAzM7PC/LRR6zIkLQX+XFbcC1hWg+G0t856XLUaV3v32x7x2ypmkTir27Y38Mpq9mmrpz3/jW8bEZu3VMkJhXVpkkZFxLBaj6OtddbjqtW42rvf9ojfVjGLxFndtpIaq3nctbWdzvDfvG95WFf3YK0H0E4663HValzt3W97xG+rmEXidNZ/R/ZRNX+vPENhZmZtyjMUXZNnKMzMrK2NqvUArON5hsLMzMwK8wyFmZmZFeaEwszMzApzQmFmZmaFrVPrAZiZ2dpN0gbADcC7wMSIuL3GQ7J24BkKMzNrNUm3SHpZ0sKy8s9LelbSHyWNSMVfBO6OiLOBozt8sNYhnFCYmdnqGA18Pl8gqRvwU+BwoB9wkqR+wCeBv6RqKztwjNaBnFCYmVmrRcQk4NWy4kHAHyPihYh4F7gTOAb4K1lSAb7urLX8xpqZWVvZilUzEZAlElsB9wLHSfoZneBPRFv78KJMMzNrK6pQFhHxJnBGRw/GOpZnKMzMrK38Fdg69/qTwEs1Got1MCcUZmbWVmYCfSVtJ6k78CXggRqPyTqIEwozM2s1SXcAU4GdJP1V0pkR8R5wDjAOeBr4bUQsquU4reP44WBmZmZWmGcozMzMrDAnFGZmZlaYEwozMzMrzAmFmZmZFeaEwszMzApzQmFmZmaFOaEwMzOzwpxQmJmZWWFOKMzMzKyw/w/geSkrJjT9lwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.xscale(\"log\")\n",
    "grid.score(X_test, y_test)\n",
    "important = np.argsort(np.abs(grid.best_estimator_.coef_)).ravel()\n",
    "plt.barh(range(10), grid.best_estimator_.coef_.ravel()[important[-10:]])\n",
    "plt.yticks(range(10), X_train.columns[important[-10:]]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T11:54:35.377355Z",
     "start_time": "2019-07-04T11:54:35.373842Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# %load solutions/adult_classification.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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