sem_26.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9268e763",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Семинар 26. Модели бинарного выбора.\n",
    "\n",
    "import numpy as np # математические операции\n",
    "import pandas as pd # операции с табличками\n",
    "import statsmodels.api as sm # модели \n",
    "import statsmodels.formula.api as smf # модели \n",
    "from sklearn.linear_model import LinearRegression # линейная регрессия без статистики\n",
    "import seaborn as sns # графики\n",
    "import matplotlib.pyplot as plt # графики\n",
    "from statsmodels.iolib.summary2 import summary_col # несколько моделей в одной таблице\n",
    "from statsmodels.graphics.mosaicplot import mosaic\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from sklearn.metrics import plot_confusion_matrix, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d7a4f9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: rdatasets in ./opt/anaconda3/lib/python3.9/site-packages (0.1.0)\r\n",
      "Requirement already satisfied: pandas in ./opt/anaconda3/lib/python3.9/site-packages (from rdatasets) (1.3.4)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in ./opt/anaconda3/lib/python3.9/site-packages (from pandas->rdatasets) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in ./opt/anaconda3/lib/python3.9/site-packages (from pandas->rdatasets) (2021.3)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in ./opt/anaconda3/lib/python3.9/site-packages (from pandas->rdatasets) (1.20.3)\r\n",
      "Requirement already satisfied: six>=1.5 in ./opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.7.3->pandas->rdatasets) (1.16.0)\r\n"
     ]
    }
   ],
   "source": [
    "get_ipython().system('pip install rdatasets # наборы данных')\n",
    "# !pip install yfinance\n",
    "from rdatasets import data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3cb0f3f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inlf</th>\n",
       "      <th>hours</th>\n",
       "      <th>kidslt6</th>\n",
       "      <th>kidsge6</th>\n",
       "      <th>age</th>\n",
       "      <th>educ</th>\n",
       "      <th>wage</th>\n",
       "      <th>repwage</th>\n",
       "      <th>hushrs</th>\n",
       "      <th>husage</th>\n",
       "      <th>...</th>\n",
       "      <th>mtr</th>\n",
       "      <th>motheduc</th>\n",
       "      <th>fatheduc</th>\n",
       "      <th>unem</th>\n",
       "      <th>city</th>\n",
       "      <th>exper</th>\n",
       "      <th>nwifeinc</th>\n",
       "      <th>lwage</th>\n",
       "      <th>expersq</th>\n",
       "      <th>logw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1610</td>\n",
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       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>3.3540</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2708</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7215</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>10.910060</td>\n",
       "      <td>1.210154</td>\n",
       "      <td>196</td>\n",
       "      <td>1.471095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1656</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>1.3889</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2310</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6615</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>19.499981</td>\n",
       "      <td>0.328512</td>\n",
       "      <td>25</td>\n",
       "      <td>0.870833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>4.5455</td>\n",
       "      <td>4.04</td>\n",
       "      <td>3072</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6915</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>12.039910</td>\n",
       "      <td>1.514138</td>\n",
       "      <td>225</td>\n",
       "      <td>1.712987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>456</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0965</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1920</td>\n",
       "      <td>53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7815</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6.799996</td>\n",
       "      <td>0.092123</td>\n",
       "      <td>36</td>\n",
       "      <td>0.740269</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1568</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>4.5918</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2000</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6215</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>20.100058</td>\n",
       "      <td>1.524272</td>\n",
       "      <td>49</td>\n",
       "      <td>1.721301</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>748</th>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3020</td>\n",
       "      <td>43</td>\n",
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       "      <td>0.6215</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>28.200001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2056</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7715</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2383</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7515</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7.5</td>\n",
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       "      <td>4</td>\n",
       "      <td>9.952000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1705</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6215</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>24.983999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3120</td>\n",
       "      <td>48</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6915</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>28.363001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>753 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     inlf  hours  kidslt6  kidsge6  age  educ    wage  repwage  hushrs  \\\n",
       "0       1   1610        1        0   32    12  3.3540     2.65    2708   \n",
       "1       1   1656        0        2   30    12  1.3889     2.65    2310   \n",
       "2       1   1980        1        3   35    12  4.5455     4.04    3072   \n",
       "3       1    456        0        3   34    12  1.0965     3.25    1920   \n",
       "4       1   1568        1        2   31    14  4.5918     3.60    2000   \n",
       "..    ...    ...      ...      ...  ...   ...     ...      ...     ...   \n",
       "748     0      0        0        2   40    13  0.0000     0.00    3020   \n",
       "749     0      0        2        3   31    12  0.0000     0.00    2056   \n",
       "750     0      0        0        0   43    12  0.0000     0.00    2383   \n",
       "751     0      0        0        0   60    12  0.0000     0.00    1705   \n",
       "752     0      0        0        3   39     9  0.0000     0.00    3120   \n",
       "\n",
       "     husage  ...     mtr  motheduc  fatheduc  unem  city  exper   nwifeinc  \\\n",
       "0        34  ...  0.7215        12         7   5.0     0     14  10.910060   \n",
       "1        30  ...  0.6615         7         7  11.0     1      5  19.499981   \n",
       "2        40  ...  0.6915        12         7   5.0     0     15  12.039910   \n",
       "3        53  ...  0.7815         7         7   5.0     0      6   6.799996   \n",
       "4        32  ...  0.6215        12        14   9.5     1      7  20.100058   \n",
       "..      ...  ...     ...       ...       ...   ...   ...    ...        ...   \n",
       "748      43  ...  0.6215        10        10   9.5     1      5  28.200001   \n",
       "749      33  ...  0.7715        12        12   7.5     0     14  10.000000   \n",
       "750      43  ...  0.7515        10         3   7.5     0      4   9.952000   \n",
       "751      55  ...  0.6215        12        12  14.0     1     15  24.983999   \n",
       "752      48  ...  0.6915         7         7  11.0     1     12  28.363001   \n",
       "\n",
       "        lwage  expersq      logw  \n",
       "0    1.210154      196  1.471095  \n",
       "1    0.328512       25  0.870833  \n",
       "2    1.514138      225  1.712987  \n",
       "3    0.092123       36  0.740269  \n",
       "4    1.524272       49  1.721301  \n",
       "..        ...      ...       ...  \n",
       "748       NaN       25  0.000000  \n",
       "749       NaN      196  0.000000  \n",
       "750       NaN       16  0.000000  \n",
       "751       NaN      225  0.000000  \n",
       "752       NaN      144  0.000000  \n",
       "\n",
       "[753 rows x 23 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# рассмотрим данные о женщинах MROZ\n",
    "t = pd.read_stata('/Users/polinapogorelova/Downloads/mroz.dta')\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c545558",
   "metadata": {},
   "outputs": [],
   "source": [
    "# преобразуем все качественные переменные (кроме зависимой inlf)\n",
    "for col in ['city']:\n",
    "  t[col] = pd.Categorical(t[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d11d6011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAJTklEQVR4nO3cz4vdd73H8dfnnGMO02Q6MBiGwbRYUrGWq1gpLtx1YemFC9lq6aqWrvwD7i52d7cXrptwCVIKissKgtkIbly0UBWtXOkYIYP5RROaGJKTTuZzF6m9CUnuTMvM+Z738HjsZgiHF8knT06+cz5pvfcAUMdo6AEAfDbCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDGT/XjR1lpv+/HCHHhtdPe9xHbfHngJzFlPet9dOvcn3ElOfvnL+/HSHHAnz55Nkjz1xlMDL4H5+tuP/rbrdyselQAUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxUyGHgAshu3Zdi68eSEfX/o466+t59DaoaEn8QjCPUdnrlzJ5myWlckkJ774xUxaG3oSfKpNWtZeXsuVM1eGnsIOPCqZk/OzWa7fuZNX19dz9AtfyPs3bgw9Ce7Txi3jw+OhZ7ALwj0n52azHF9aSpI8vbSUc7PZwIuAqoR7Tm5tb2f6yaOR6WiUm9vbAy8CqhLuOVkajTLrPcndiC+N/NYDn496zMmx6TQbN28mSTZu3syT0+nAi+BBF9+6mFsbt/LhLz7MP373j6Hn8Ag7fqqktXY6yb8ludR7/5f9n3QwrU+nWR6Pc/r8+axMJvnOysrQk+ABa6+sDT2BXdjNxwF/kuS/kry5v1MOvhdXV4eeABwAOz4q6b3/JokPdgIsiD27gNNaez3J63v1egA83J6Fu/d+KsmpJBm11vfqdQ+S2fZ23rxwIZc+/jivra9n7ZArxSwOV97r8KmSOZq0lpfX1vLsY48NPQUe8M8r748963wuOuGeo3FrOTx2pZjF5Mp7HTuGu7X20yS/TfLV1tpma+0H+z8LgEfZ8Rl37/378xgCwO54VAJQjP+Pe87eungxF27fzodbW3l+eTnfPHJk6EnwqYtvXcztC7ez9eFWlp9fzpFvOp+LSLjn7JU1V4pZXK681+BRCUAxwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMVMhh4AD3P25NmhJ8BctR+1Xf/afQl3G41y8qy/eHx+L7zxwtATYGHtS7j79nbeeMFfPD67k7/+9dATYOF5xg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMVMhh4ALIat2VZ+/+bvc+PSjXzrtW/lyNqRoSfxCMI9R2c2NrJ57VpWptOceOaZTEb+wcPiGE1G+frLX8/GmY2hp7AD5ZiT89ev5/psllefey5HDx/O+5cvDz0J7jMaj3Lo8KGhZ7ALwj0n565dy/HV1STJ06urOffRRwMvAqoS7jm5tbWV6XicJJmOx7m5tTXwIqAq4Z6Tpckkszt3ktyN+NLEjxeAz0e45+TY449n48qVJMnG1at5cmVl4EXwoD+89Ydc3biav/ziL7nwuwtDz+ERdvW2r7X2UpL/TDJO8t+99//Y11UH0Prycpan05x+772sTKf5zhNPDD0JHvCNV74x9AR2Ycdwt9bGSX6c5LtJNpO801p7u/f+/n6PO2hePH586AnAAbCbRyXfTvJB7/2vvffbSX6W5MT+zgLgUXYT7i8lOXfP15uffO8+rbXXW2vvttbe7Xu1DoAH7OYZd3vI9x5oc+/9VJJTSTJqTbsfws1JFt3GmY1c27yW6co0z5x4JqOJM7qIdvOnspnk3p+kHUvy9/2Zc3C5Ocmiu37+embXZ3nu1edy+OjhXH7fGV1Uuwn3O0m+0lp7qrV2KMn3kry9v7MOHjcnWXTXzl3L6vG7Z3T16dV8dM4ZXVQ7hrv3vpXkh0l+leTPSX7ee//Tfg87aNycZNFt3drKeHr3jI6n42zddEYX1a4+x917/2WSX+7zlgPNzUkW3WRpkjuzu2d069ZWJkvO6KLyk4c5cXOSRff4scdzZePuGb26cTUrTzqji0q45+Tem5OXb9zI144eHXoS3Gd5fTnT5WneO/1ebly+kaNfc0YXlX8LzZGbkyy64y86oxV4xw1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFCPcAMUIN0Axwg1QjHADFNN673v/oq31JNt7/sIHQ0uy97/psHec0WGMeu9tN79wX8LNo7XW3u29Pz/0DngUZ3TxeVQCUIxwAxQj3PN3augBsANndMF5xg1QjHfcAMUI9xy11l5qrf1Pa+2D1tq/D70H7tVaO91au9Ra++PQW/j/CfectNbGSX6c5F+TPJvk+621Z4ddBff5SZKXhh7BzoR7fr6d5IPe+19777eT/CzJiYE3wad6779JcmXoHexMuOfnS0nO3fP15iffA/hMhHt+HnaV1Ud6gM9MuOdnM8kT93x9LMnfB9oCFCbc8/NOkq+01p5qrR1K8r0kbw+8CShIuOek976V5IdJfpXkz0l+3nv/07Cr4P+01n6a5LdJvtpa22yt/WDoTTycm5MAxXjHDVCMcAMUI9wAxQg3QDHCDVCMcAMUI9wAxQg3QDH/C2MyuDjfFATDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# мозаичный график (для качественных переменных)\n",
    "mosaic(t, ['inlf', 'city']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "72f73747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ящик с усами\n",
    "sns.boxplot(data=t, x='inlf', y='age');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d67fffec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=t, x='age', kind='kde', hue='inlf', multiple='stack');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "faaf1b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=t, x='age', kind='kde', hue='inlf', multiple='fill');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "681178b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.533553\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>inlf</td>       <th>  No. Observations:  </th>  <td>   753</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   745</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     7</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 09 Apr 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.2197</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:46:42</td>     <th>  Log-Likelihood:    </th> <td> -401.77</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -514.87</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>3.159e-45</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.4255</td> <td>    0.860</td> <td>    0.494</td> <td> 0.621</td> <td>   -1.261</td> <td>    2.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>educ</th>      <td>    0.2212</td> <td>    0.043</td> <td>    5.091</td> <td> 0.000</td> <td>    0.136</td> <td>    0.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exper</th>     <td>    0.2059</td> <td>    0.032</td> <td>    6.422</td> <td> 0.000</td> <td>    0.143</td> <td>    0.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>expersq</th>   <td>   -0.0032</td> <td>    0.001</td> <td>   -3.104</td> <td> 0.002</td> <td>   -0.005</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>   -0.0880</td> <td>    0.015</td> <td>   -6.040</td> <td> 0.000</td> <td>   -0.117</td> <td>   -0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidslt6</th>   <td>   -1.4434</td> <td>    0.204</td> <td>   -7.090</td> <td> 0.000</td> <td>   -1.842</td> <td>   -1.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidsge6</th>   <td>    0.0601</td> <td>    0.075</td> <td>    0.804</td> <td> 0.422</td> <td>   -0.086</td> <td>    0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nwifeinc</th>  <td>   -0.0213</td> <td>    0.008</td> <td>   -2.535</td> <td> 0.011</td> <td>   -0.038</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                   inlf   No. Observations:                  753\n",
       "Model:                          Logit   Df Residuals:                      745\n",
       "Method:                           MLE   Df Model:                            7\n",
       "Date:                Sun, 09 Apr 2023   Pseudo R-squ.:                  0.2197\n",
       "Time:                        21:46:42   Log-Likelihood:                -401.77\n",
       "converged:                       True   LL-Null:                       -514.87\n",
       "Covariance Type:            nonrobust   LLR p-value:                 3.159e-45\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.4255      0.860      0.494      0.621      -1.261       2.112\n",
       "educ           0.2212      0.043      5.091      0.000       0.136       0.306\n",
       "exper          0.2059      0.032      6.422      0.000       0.143       0.269\n",
       "expersq       -0.0032      0.001     -3.104      0.002      -0.005      -0.001\n",
       "age           -0.0880      0.015     -6.040      0.000      -0.117      -0.059\n",
       "kidslt6       -1.4434      0.204     -7.090      0.000      -1.842      -1.044\n",
       "kidsge6        0.0601      0.075      0.804      0.422      -0.086       0.207\n",
       "nwifeinc      -0.0213      0.008     -2.535      0.011      -0.038      -0.005\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# оценим logit модель\n",
    "fit_logit = smf.logit('inlf ~ educ + exper + expersq + age + kidslt6 + kidsge6 + nwifeinc', data=t).fit()\n",
    "fit_logit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6ca6264b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.532938\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Probit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>inlf</td>       <th>  No. Observations:  </th>  <td>   753</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                <td>Probit</td>      <th>  Df Residuals:      </th>  <td>   745</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     7</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 09 Apr 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.2206</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:46:42</td>     <th>  Log-Likelihood:    </th> <td> -401.30</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -514.87</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>2.009e-45</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.2701</td> <td>    0.509</td> <td>    0.531</td> <td> 0.595</td> <td>   -0.727</td> <td>    1.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>educ</th>      <td>    0.1309</td> <td>    0.025</td> <td>    5.183</td> <td> 0.000</td> <td>    0.081</td> <td>    0.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exper</th>     <td>    0.1233</td> <td>    0.019</td> <td>    6.590</td> <td> 0.000</td> <td>    0.087</td> <td>    0.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>expersq</th>   <td>   -0.0019</td> <td>    0.001</td> <td>   -3.145</td> <td> 0.002</td> <td>   -0.003</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>   -0.0529</td> <td>    0.008</td> <td>   -6.235</td> <td> 0.000</td> <td>   -0.069</td> <td>   -0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidslt6</th>   <td>   -0.8683</td> <td>    0.119</td> <td>   -7.326</td> <td> 0.000</td> <td>   -1.101</td> <td>   -0.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidsge6</th>   <td>    0.0360</td> <td>    0.043</td> <td>    0.828</td> <td> 0.408</td> <td>   -0.049</td> <td>    0.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nwifeinc</th>  <td>   -0.0120</td> <td>    0.005</td> <td>   -2.484</td> <td> 0.013</td> <td>   -0.022</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          Probit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                   inlf   No. Observations:                  753\n",
       "Model:                         Probit   Df Residuals:                      745\n",
       "Method:                           MLE   Df Model:                            7\n",
       "Date:                Sun, 09 Apr 2023   Pseudo R-squ.:                  0.2206\n",
       "Time:                        21:46:42   Log-Likelihood:                -401.30\n",
       "converged:                       True   LL-Null:                       -514.87\n",
       "Covariance Type:            nonrobust   LLR p-value:                 2.009e-45\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.2701      0.509      0.531      0.595      -0.727       1.267\n",
       "educ           0.1309      0.025      5.183      0.000       0.081       0.180\n",
       "exper          0.1233      0.019      6.590      0.000       0.087       0.160\n",
       "expersq       -0.0019      0.001     -3.145      0.002      -0.003      -0.001\n",
       "age           -0.0529      0.008     -6.235      0.000      -0.069      -0.036\n",
       "kidslt6       -0.8683      0.119     -7.326      0.000      -1.101      -0.636\n",
       "kidsge6        0.0360      0.043      0.828      0.408      -0.049       0.121\n",
       "nwifeinc      -0.0120      0.005     -2.484      0.013      -0.022      -0.003\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# оценим probit модель\n",
    "fit_probit = smf.probit('inlf ~ educ + exper + expersq + age + kidslt6 + kidsge6 + nwifeinc', data=t).fit()\n",
    "fit_probit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f7041d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>       <th>inlf I</th>   <th>inlf II</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>0.4255</td>   <td>0.2701</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>          <td>(0.8604)</td> <td>(0.5086)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>educ</th>       <td>0.2212</td>   <td>0.1309</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>          <td>(0.0434)</td> <td>(0.0253)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exper</th>      <td>0.2059</td>   <td>0.1233</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>          <td>(0.0321)</td> <td>(0.0187)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>expersq</th>    <td>-0.0032</td>  <td>-0.0019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>          <td>(0.0010)</td> <td>(0.0006)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>        <td>-0.0880</td>  <td>-0.0529</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>          <td>(0.0146)</td> <td>(0.0085)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidslt6</th>    <td>-1.4434</td>  <td>-0.8683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>          <td>(0.2036)</td> <td>(0.1185)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidsge6</th>    <td>0.0601</td>   <td>0.0360</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>          <td>(0.0748)</td> <td>(0.0435)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nwifeinc</th>   <td>-0.0213</td>  <td>-0.0120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>          <td>(0.0084)</td> <td>(0.0048)</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "===========================\n",
       "           inlf I  inlf II \n",
       "---------------------------\n",
       "Intercept 0.4255   0.2701  \n",
       "          (0.8604) (0.5086)\n",
       "educ      0.2212   0.1309  \n",
       "          (0.0434) (0.0253)\n",
       "exper     0.2059   0.1233  \n",
       "          (0.0321) (0.0187)\n",
       "expersq   -0.0032  -0.0019 \n",
       "          (0.0010) (0.0006)\n",
       "age       -0.0880  -0.0529 \n",
       "          (0.0146) (0.0085)\n",
       "kidslt6   -1.4434  -0.8683 \n",
       "          (0.2036) (0.1185)\n",
       "kidsge6   0.0601   0.0360  \n",
       "          (0.0748) (0.0435)\n",
       "nwifeinc  -0.0213  -0.0120 \n",
       "          (0.0084) (0.0048)\n",
       "===========================\n",
       "Standard errors in\n",
       "parentheses.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# сравним оценки коэффициентов в logit и probit моделях\n",
    "summary_col([fit_logit, fit_probit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c873e056",
   "metadata": {},
   "outputs": [],
   "source": [
    "# создадим новые наблюдения\n",
    "new = pd.DataFrame({'age': np.arange(5, 100, step=0.5)})\n",
    "new['educ'] = 10\n",
    "new['exper'] = 4\n",
    "new['expersq'] = 16\n",
    "new['kidslt6'] = 1\n",
    "new['kidsge6'] = 0\n",
    "new['nwifeinc'] = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "62569c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>educ</th>\n",
       "      <th>exper</th>\n",
       "      <th>expersq</th>\n",
       "      <th>kidslt6</th>\n",
       "      <th>kidsge6</th>\n",
       "      <th>nwifeinc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
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       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  educ  exper  expersq  kidslt6  kidsge6  nwifeinc\n",
       "0  5.0    10      4       16        1        0        16\n",
       "1  5.5    10      4       16        1        0        16\n",
       "2  6.0    10      4       16        1        0        16\n",
       "3  6.5    10      4       16        1        0        16\n",
       "4  7.0    10      4       16        1        0        16"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f66a1dad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>educ</th>\n",
       "      <th>exper</th>\n",
       "      <th>expersq</th>\n",
       "      <th>kidslt6</th>\n",
       "      <th>kidsge6</th>\n",
       "      <th>nwifeinc</th>\n",
       "      <th>phat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.765876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.757892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.749725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.741376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.732848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  educ  exper  expersq  kidslt6  kidsge6  nwifeinc      phat\n",
       "0  5.0    10      4       16        1        0        16  0.765876\n",
       "1  5.5    10      4       16        1        0        16  0.757892\n",
       "2  6.0    10      4       16        1        0        16  0.749725\n",
       "3  6.5    10      4       16        1        0        16  0.741376\n",
       "4  7.0    10      4       16        1        0        16  0.732848"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# спрогнозируем для новых женщин вероятность того, что inlf = 1 (будут работать)\n",
    "new['phat'] = fit_logit.predict(new)\n",
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fc8b2a88",
   "metadata": {},
   "outputs": [],
   "source": [
    "logit = sm.genmod.families.links.logit()\n",
    "probit = sm.genmod.families.links.probit()\n",
    "binomial = sm.families.Binomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f893abf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# рассмотрим альтернативную команду для оценивания моделей бинарного выбора\n",
    "fit_logit_bis = smf.glm('inlf ~ educ + exper + expersq + age + kidslt6 + kidsge6 + nwifeinc', data=t,\n",
    "                        family = binomial(logit)).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "905479b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>inlf</td>       <th>  No. Observations:  </th>  <td>   753</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   745</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>     7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -401.77</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 09 Apr 2023</td> <th>  Deviance:          </th> <td>  803.53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:47:54</td>     <th>  Pearson chi2:      </th>  <td>  732.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>5</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.4255</td> <td>    0.860</td> <td>    0.494</td> <td> 0.621</td> <td>   -1.261</td> <td>    2.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>educ</th>      <td>    0.2212</td> <td>    0.043</td> <td>    5.091</td> <td> 0.000</td> <td>    0.136</td> <td>    0.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exper</th>     <td>    0.2059</td> <td>    0.032</td> <td>    6.422</td> <td> 0.000</td> <td>    0.143</td> <td>    0.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>expersq</th>   <td>   -0.0032</td> <td>    0.001</td> <td>   -3.104</td> <td> 0.002</td> <td>   -0.005</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>   -0.0880</td> <td>    0.015</td> <td>   -6.040</td> <td> 0.000</td> <td>   -0.117</td> <td>   -0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidslt6</th>   <td>   -1.4434</td> <td>    0.204</td> <td>   -7.090</td> <td> 0.000</td> <td>   -1.842</td> <td>   -1.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidsge6</th>   <td>    0.0601</td> <td>    0.075</td> <td>    0.804</td> <td> 0.422</td> <td>   -0.086</td> <td>    0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nwifeinc</th>  <td>   -0.0213</td> <td>    0.008</td> <td>   -2.535</td> <td> 0.011</td> <td>   -0.038</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:                   inlf   No. Observations:                  753\n",
       "Model:                            GLM   Df Residuals:                      745\n",
       "Model Family:                Binomial   Df Model:                            7\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -401.77\n",
       "Date:                Sun, 09 Apr 2023   Deviance:                       803.53\n",
       "Time:                        21:47:54   Pearson chi2:                     732.\n",
       "No. Iterations:                     5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.4255      0.860      0.494      0.621      -1.261       2.112\n",
       "educ           0.2212      0.043      5.091      0.000       0.136       0.306\n",
       "exper          0.2059      0.032      6.422      0.000       0.143       0.269\n",
       "expersq       -0.0032      0.001     -3.104      0.002      -0.005      -0.001\n",
       "age           -0.0880      0.015     -6.040      0.000      -0.117      -0.059\n",
       "kidslt6       -1.4434      0.204     -7.090      0.000      -1.842      -1.044\n",
       "kidsge6        0.0601      0.075      0.804      0.422      -0.086       0.207\n",
       "nwifeinc      -0.0213      0.008     -2.535      0.011      -0.038      -0.005\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_logit_bis.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f1b765b2",
   "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",
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       "    }\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>mean</th>\n",
       "      <th>mean_se</th>\n",
       "      <th>mean_ci_lower</th>\n",
       "      <th>mean_ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.765876</td>\n",
       "      <td>0.098079</td>\n",
       "      <td>0.528248</td>\n",
       "      <td>0.905272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.757892</td>\n",
       "      <td>0.099175</td>\n",
       "      <td>0.520444</td>\n",
       "      <td>0.900294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.749725</td>\n",
       "      <td>0.100202</td>\n",
       "      <td>0.512620</td>\n",
       "      <td>0.895088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.741376</td>\n",
       "      <td>0.101155</td>\n",
       "      <td>0.504780</td>\n",
       "      <td>0.889648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.732848</td>\n",
       "      <td>0.102030</td>\n",
       "      <td>0.496926</td>\n",
       "      <td>0.883966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.000951</td>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.005468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.000910</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.005305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.005148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.004995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.000798</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.004847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean   mean_se  mean_ci_lower  mean_ci_upper\n",
       "0    0.765876  0.098079       0.528248       0.905272\n",
       "1    0.757892  0.099175       0.520444       0.900294\n",
       "2    0.749725  0.100202       0.512620       0.895088\n",
       "3    0.741376  0.101155       0.504780       0.889648\n",
       "4    0.732848  0.102030       0.496926       0.883966\n",
       "..        ...       ...            ...            ...\n",
       "185  0.000951  0.000850       0.000165       0.005468\n",
       "186  0.000910  0.000820       0.000156       0.005305\n",
       "187  0.000871  0.000791       0.000147       0.005148\n",
       "188  0.000833  0.000763       0.000139       0.004995\n",
       "189  0.000798  0.000735       0.000131       0.004847\n",
       "\n",
       "[190 rows x 4 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = fit_logit_bis.get_prediction(new).summary_frame()\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "03e5d884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7f8f3474ec10>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nFalsY+0j+DLwduBZIAn8Sea5SSsa9esSOVceE7CuueA0Fk9L8Xf3b6Wjr3Bf4auqfPNQOQSeiJTGmCeUOed2AmHn3JBz7l+BSwIrVZmIxXwYDA6WvvkkHglzw/o1HOrq40u/2l7Yc8f9cNLJNzhYRMZirEHQbWYxYIuZ/b2Z/SWQCrBcZSMeh4UL/TfmUi/YtnJOLe9sWMpPn27ioV0HCnbeeNz3h5TL0FkRKa6xBsG7MsdeB3QBC4G3BlWocpNIwKJF/sOy1MMt37duGafW1XDL/U9ytKdwO9Mnk9qzQKRSjXXU0B7AgLnOuZuccx/ONBVVjGTSdyB3dZU2DKLhEJ9av5ojvf184cFtBTtvJOJD4MiRgp1SRCaIsY4aeiN+naG7M4/XTLYJZWNRXe3DoNR7GSyfOZX3rVvGz3+3nweebS7YeZNJDScVqURjbRr6DLAOOALgnNsC1AdRoHJXU+MnnZV6xdJ3nbOUFbOm8o+/eIr27sIMawqFfM1Aw0lFKstYg2DQOaeNDjOmToU5c/widaUKg0imiah7YJB/+MWTFGo9wGTSDycth8l0IlIcY150zszeAYTNbJmZ/TPwcIDlKnvTpvlVS0u5y9kpM2r40/OX8+BzB7h3x/6CnTeRgAMHNJxUpFKMNQj+HDgD6AP+CzgK/EVQhZooZszw6/SUctjl1WtP4ay50/jCg0/R2lmYWWFanVSksow1CFZmbhEgAVwFbAiqUBOFmd/hrLa2dB+a4ZDxqfWr6R9Kc8sDWwvWRFRV5YeTlnq4rIgEb6xB8J/AN4G3AG/I3N74cr9kZleY2Q4z22lm15/kuHMz6xe9bYzlKRtmfu/jKVNKVzNYWJvi/Reu4JHdrdy1rbEg5wxn9sE5VNjVr0WkDI01CFqdc3c553Y55/Zkbyf7BTMLA7cCV+JrE283s5UnOO7zwD3jLHvZKIeNbd62up5zFszgS7/eTtORroKcM5n0W3iWw1pLIhKcsQbBp83s62b2djN7S/b2Mr+zDtjpnHveOdcP3I5vUhrpz4HvAwfHXuzyEwqVdmObkPkmokjIuOneLQwWYKKDme8vUMexyOQ21iB4L7AGuALfJPRGfPPQycwHhrdTNGWee4GZzQfeDHxtjOUoa9mNbWKx0gy/nF2T5KOvPottLUf41obCTPyOx32wqeNYZPIa63ZXq51zZ43z3KNtiz7ye+UXgY8554bsJLuom9k1wDUAixYtGmcxiiu7sU1jow+DZLK41790+Twe2nWQf3tsJ+ctmsmZc6flfc5Uynccp1LH+w5EZPIYa43gt6O177+MJvzidFkLgJGD3RuA281sN/A24Ctm9nsjT+Scu8051+Cca5g5c+Y4i1F84bBfvjocLk3N4K8uOYOZ1QluuncL3f35L5mqjmORyW2sQXARfgnqHWa21cyeNLOtL/M7G4BlZrYks4T11cCL1idyzi1xztU75+qB7wEfcM79aHxvoTxFIj4MSrHLWXU8yo2XrWH/0e6C7V2QTEJ7uzawEZmMxto0dMV4T+ycGzSz6/CjgcLAN51z28zs2szrk6Jf4GQiEb+XQWOj/wBNJIp37TXzp/OuhqV8e+NzXLhkFq9aOiev85n5/oKWFli82D8WkcnBCjUBqVgaGhrcxo0bS12Mcenv92EQCvkP02IZGEpzzXcfoqWjh3//w4upS+WfRJ2dft5EbW3+5ROR4jGzTc65htFeG/NWlZK7WMzXDIaGfCgUSzQc4tOXr6V3cIjP/nwr6QKEfnbGsZaqFpk8FARFkg2DgYHihkH99Go++MqVPLq3lds3P5/3+UIh33l8cELP+hCR4RQERRSP+y0vix0Gv3fmIi5ZOoevPryDbS2H8z5fMunnFWhugcjkoCAosnjc1wz6+4vXvGJmXP/aVcxMJbjx7s109OV/4aoqP+NYi9KJTHwKghJIJHzNoK+veGEwJRHlpivWcrCjl8/fn/9GNpHMeDPtZiYy8SkISqQUYXDW3Glcc8FpPLCzmTsKsEppVZVflK4UayuJSOEoCEookfDNRL29xQuDPzznFNYtquOLD27jubZjeZ8vmfRzC9REJDJxKQhKLJn0NYNihUHIjBsvW0NNPMoNP9tMz0B+S1BEoz4EtPyEyMSlICgDxQ6D6VVxbrxsDXsOd/LFB/NfgqKqyi8/oSYikYlJQVAmih0G5y6q410NS7lreyP3PLMvr3OZqYlIZCJTEJSRbBgUqwP5T85fzup50/n8A0/y3KH8JgVkm4g0ikhk4lEQlJlihkEkFOLmK9eSikX4xE820ZXn/IJsE1FXYXbKFJEiURCUoeFDS4OegVyXSnDzlWez/2g3n71va17zC8z85jXNzTCY/zYIIlIkCoIylQ2DYixHsWb+dD7wihX88rkWvpPnekTZiWba51hk4lAQlLFsGAwO+tpBkK5eu4RXnzqHrz60g81N+Y0Fraryy1Ufy3+agogUgYKgzGUXqkung90dzMz4xKWrmV9bxQ13b6a1M7+LpVK+VhB0gIlI/hQEE0B2CWsINgxSsQh/97pz6B0Y5PqfbKRvMPexoKGQH0nU3OxDTETKl4JggsiGgRn09AR3nSUzarjxsjU8feAot+S5OF087vs3WlsLWEARKTgFwQQSjfowCIeDDYOLl87hT89fzj079uXdeZxK+YXptHeBSPlSEEwwkYgPg0gk2PH6f3Tuqbzm1Lnc+ptneGR37tuRDR9SWszNeERk7BQEE1A4DAsW+MlnnZ3BXMPM+OT6VZxaN4VP372ZPYdzv1A47Gsz+/erv0CkHCkIJqhwGObNg5qa4JpdktEIt7zhHCLhEB+7ayPHenP/Sh+P+zkR6i8QKT8KggksFII5c6C21o/ZD2IC19wpVXzudefQfKyHj/9kE/15jCTK9hccOVK48olI/hQEE5wZzJoFdXW+ZhBE08ua+dP55PpVbN7Xzufuz28Ziupqv0ppkJ3dIjI+CoJJwMwHwezZvs8giKWgLzttPtdcsJx7d+zn64/+LufzhEK+b2PfvuLtyiYiJ6cgmESmTYP58/1ooiAWfXtPw6m8ceVC/vWxnfx4e+57HkejPhD27dP+BSLlQEEwydTUHN/gptDDNc2Mj776TM5dWMfnH3iSDXtz33wgkfA1gpYWLU4nUmoKgkmoqgoWL/bftgu9JEUkHOKzrzub+mnVfPwnm3jm4NGcz5VK+X4NbWYjUloKgkkqu1hdKFT4vYSr41G+cNU6piSifPhHj+U1x6CmxgeBRhKJlI6CYBLLLkmRSBR+4tnM6gRfevN5mMGHfvgoBzpyGwZk5sOgpSW4yXEicnIKgkkuO/Fs6tTCzzVYWJviC1eto7N/kA/96FEOd+e25nQo5JuJ9u3TsFKRUlAQVIBQyM81mDXLt8kXcqTOabOm8g9vPJfmYz381Z0bct73OBz2NZfGRu1hIFJsCoIKYQbTp/vhpd3dhR3Dv2b+dD77urN5tu0YH71rIz0DuY1djUb9ctuNjVqgTqSYFAQVpqbGjygaGChsM8wrlszmxsvWsLW5nY/cuSHnMIjFfO2gsVETzkSKRUFQgRIJHwaFXsp6/fJ53HjZGp7Y385H8wiDeNzXYBQGIsURaBCY2RVmtsPMdprZ9aO8/odmtjVze9jMVgdZHjkuO6KourqwnciXnTafGy9bw5b97Xz0ro30DuTWIZFI+DIpDESCF1gQmFkYuBW4ElgJvN3MVo44bBfwKufcKuBm4LagyiMvlV29dNYsHwaFWpbistPmc8P6NWzZd4iP3LUh5zBIJhUGIsUQZI1gHbDTOfe8c64fuB24avgBzrmHnXOHMw9/CywIsDwyimwncnZZikKN2Ll8xXw+tX41W/Yd4sN3PEZnjqOJkkl/rw5kkeAEGQTzgeErkzVlnjuRPwZ+NtoLZnaNmW00s42t2tkkEKkU1Nf7nwvVb3DFigV8+vK1PNlymOt+8Fvac5xnkEj4wNq7V0NLRYIQZBDYKM+N2hJtZq/GB8HHRnvdOXebc67BOdcwc+bMAhZRhovFfM0g229QiL0N1i+fx+ff0MCew5184HuP0JLjDOR43Hdu79mjSWcihRZkEDQBC4c9XgDsH3mQma0Cvg5c5Zw7FGB5ZAzCYd9vMHu2rxkUom3+wvpZfPH3zqO9u49r/+dhdrfntpZELOYDYe9eLUchUkhBBsEGYJmZLTGzGHA1cOfwA8xsEfAD4F3Oudx3O5GCMvN7Gyxa5DuQC/ENfPW86dz61gsYTDve/72H2d5yJKfzRKN+ddWmJr/tpYjkL7AgcM4NAtcB9wBPA991zm0zs2vN7NrMYTcCM4CvmNkWM9sYVHlk/JJJP98gHvdLU+Q7xHTZzCl87W0XkIpF+MD3H+GBZ5tzOk847JuvDhyA1lbtZyCSL8tn/9lSaGhocBs3Ki+KyTn/7fvgQR8O0Wh+52vv7uP6H2/kqZYjvP/CFbzznFMwG61L6eXL1dnpQ2HOHB8QIjI6M9vknGsY7TXNLJaXlR1iunixbyrKd3+D6VVx/vkt53Pp8nl89eFnuOX+JxkYGn/PdHYJ695e34msEUUiuVEQyJhlm4qSyfxHFcUjYT5z+Rreu+5U7treyIfveIyjPblNFEgmfSjs2eObsERkfBQEMi6RiN/fYO5cXzPI51t4yIw/Pf80bli/mq37D/O+//4NO3Lc+jIe94Gwb59vwirE0FeRSqEgkHEz8xvdLF7sf+7szK/D9srTF/DVt11AOu343//zMD/e3vjyvzSKcNg3FR05oslnIuOhIJCcZfdFnj7dN8nkswTEyjm1fPPqi1g1bxqfu28rt9y/lb7B8a9RZOY7j53zTUVHj2pUkcjLURBIXkIhqKvztYN0Or/awbSqOP/nqvN4d8NS7tzWyPu/9whNR3Jb7yIe9/MNmpth/34tWidyMgoCKYhk0q9VNG2a70jO9YM3HDKuvXAFt7z+HPYd7eI93/k1P97eSC7DnEMhmDLFjyravbvwezaLTBYKAimYUAhmzvSBMDSUX+3g4qVz+PY7Lub0WVP53H1bueFnmznWm/uookTC1wz279cqpiIjKQik4LK1g2zfQa6dtrNrknzpzefzgVes4MHnW3j3f/2ajY1tOZ0rHD5eO9i1y0+Q08giEU9BIIHI9h3U1/sO3I6O3D54wyHjnecs5V/+4BUkImE++MNH+fsHnsxrf4NUyi9NsXt3/pPjRCYDBYEEKrs/8qxZfjXTXBewWzFrKv/29lfyjrNP4c5te3nHfzzIr58/kNO5QiE/sigc9sNM9+1Tc5FUNgWBBC67mukpp/hgyLUzORENc91Fp3PbH7yC2mSMj/14Izf87PGcN7yJRl/cXHTwYOG26xSZSLTonBRdV5dfOXRw0A/xDOXwdWRwKM1/PP4c//roTuKREO87bxlvW1VPJJzbdxvnfG0lnfZNWlOnahE7mVxOtuicgkBKIp32k71aW30QZNcLGq89hzv50q+289s9rSyeluJDF5/BeYtz38UunT7efFVX52sMCgSZDBQEUrYGBqC93Y/iicV809F4Oed4ePdBvvir7ew72s1FS2bz5688nYW1qZzLlU77jmQzmDHDB0IkkvPpREpOQSBlr7fX1w66unwYxGLjP0f/4BD/vWU339rwLH2DaV6/cgHvW7eMWTXJnMuVrSE4B7W1/pZL2URKTUEgE0Z3t++07e31/Qe5fAtv7+7jWxt28qMn92IGb1m1mHeds5RpVfGcy5XtQxga8iOOpk/3gZVLc5ZIKSgIZELJ7jzW2uqbjpLJ3AKh+Vg333j0We5+polEJMxbVtXzv9bUMyOVQ/vTML29vlzRqG82yg5FFSlnCgKZkJzzE9Ha2vwIo0Qit0DY3d7BNx59ll/sbCYSCvH6lQt4x9lLmT+1Kq/yDQ76UADfhzB1qmoJUr4UBDKhZVc1bWvz38QTidz2TW480sV/bnqOnz29j7RzvHb5XP5gzRJWzq7Nq3zO+UAYHPTlmjbNz15WX4KUEwWBTArZJqO2Nr9+UTzub+PV2tnL7Zuf546n9tI9MMTK2bW8ddViXrtsLrFIfm08Q0M+FNJpH1jTpuXe1yFSSAoCmVSyHbdtbf4+EsmtSaarb4CfPbOP723dzd7DXdQmY7zpjIW8fuXCvIaeZg0M+MByzvdz1Nb6+1xqMyL5UhDIpNXb67emPHrUT0xLJMbfceucY2PjIb63dTcP7TpA2sHqedN4/ekLefWyuaRi+X+d7+/3N+d8LWbqVF9TiMXUpyDFoSCQSW9w0Hcst7cfb6vPZXJaa2cvP3umiZ8+3cTew10kImFetXQOly6fy7kL6/JuOsqWNVtTyG6ek0rlFmIiY6UgkIqRbTY6fNj3J4RC/hv4eNvonXM81XKEn2xv5Bc7W+joG6A6FuHipXN4zTIfCtEc1zUaLp32oZBd7C6R8MGQSPhy57IOk8hoFARSkQYG/AS1Q4f8z+Gw/4Ad74frwFCajY1t3P9sM796roXO/kGqohHWLarjwiWzuGDxzLznJgwvc7YJCXx5a2qOz7ZWjUFypSCQiuac/9bd2en7EgYHfQ0hl2/c/YNDbGhs4ze7DvLwroO0dvmJBCtmTeXC+llcWD+L5bOmECnQV/n+fh8O6bTvS4jF/AS2bKdzNKo+BhkbBYFIRnbMfzYU0unjzUe5dDLvbOvgod0HeHjXQba1HMEBqViE1fOmc/aC6aydP4PlM6cSDhXm03pw0AfD4KAPALPju67F4z4YIhGFg7yUgkBkFNmaQleXD4WBAf8BGo3mNprnSE8/j+1tZcu+dh7fd4i9h7sAHwxr5k3nrHnTWDm7ltNnTSUVL8wYUueOB8PQkH8uO3qqqur4bOxoVP0NlU5BIDIG/f2+ttDR4cMhO6onFstt7H9bVy+b97WzuenQi4LBgPrp1Zw+u5Yz5tRy+uxalkyvJl6AEUlwPByGhvzNOR9q2fkWyaR/T5GIv6nfoTIoCETGKZ32wdDT44Oht/d4MEQiudUYjvX2s/3AUZ4+cIRtLUfY3nKEI71+s+SwGQtrUyytq2FZ3RSW1tVwat0UZlUnsAK18wwNHa85pNPHO6TDYd+slO2QzjYvhcMKiclEQSCSp2wwZDudu7v9B6lzx79Zj7dt3jlH87Eenjl4lOcOHePZ1g6eO3SM5mM9LxxTFQ2zsDbFwmnVLKpNsWhaioW1KRbVpgrWvJROvzgksjUI53wQRKPHl/OIRo8HhIJiYlEQiBRYtvllYMDXFrq7j9cahtcccml66ewb4PlDHexs62DP4U4aj3Sx93AXLR3dpIf9c51RFWfulCRzplQxtybJnClJ5tRkblOSJKP5z4h27nhIpNP+Nly2ySlbk8jewmH/N8jeq3+i9BQEIkXg3PFRPdlmpezeBcNlv0lHIuP7gOwfHGLf0W72HunKhEMnzcd6aD7Ww8HOHgbTL/63PDURZVZ1kulVcWak4szI3NelEkyvilOXijO9Kk5VHktoOHe8RjGyyWm4bChkQ2N4SI4MjFBIo56CcLIg0JqIIgWSHXEUjfoRO7W1/vl02gfE8JDo7T0+R2D4h57Ziz8Yw+Hjr8ciYZbMqGHJjJqXXDvtHIe6+mjp6Kb5WA8tHT0c6OihtbOPQ9297Grv4FB3H0Ppl35KJ6NhpiZiTE1EmZKIMSURZerI+2SMKXF/Xx2LUBWLEAuHMLMxNREND4xsR/zwGsbID/5sIGTDY3hwZP822b/V8J+z9zI+gQaBmV0BfAkIA193zt0y4nXLvP46oBv4I+fc40GWSaTYsiOPRtufYPi36WwTTHaBumxoZD8ss233w897/MPQmJ5MUJdKcNbc0cuRdo5jvQMc6urlUHcfh7r6aOvqo727j6O9/RzrHeBobz8tHT0c7e2no3eAk7UXREJGVSxCKhahKpq5H/b4hZ9jYRKRCPFIiEQkTDxzS0RDxMJhEtHMc+HQCz8b9kLtYvgqrsNrHCP/HiP/5iMDdXitI1sby87FGH470fOTuZYSWBCYWRi4FVgPNAEbzOxO59z2YYddCSzL3M4Dvpq5F6kI2Q+mkw1PzX5zzja9DO/czdYyRo4IGp0RIcbsRIw5SbC6E3/gmYHD0dU/wLG+AY719nO0d4CjPf109w/S1T9I94C/7+offOG5o7397D/WTXfmue6BoZz+LrFwiHjEB0U0HCIaDhEJmf85FCISDhEN2/GfQ6HMcZY5NkTYQkRDRiQUImRGyEKEDH9P5t6McMj8vRmhUOY+8/zw58JmhMNGJJR5LWREI/5xJGyZoMn8ThgiIQiH/blCIYiE8TWokH9sGOEQhEPmA8j8YxtxH8pMRhweUoUWZI1gHbDTOfc8gJndDlwFDA+Cq4BvO99R8VszqzWzuc655gDLJTKhDP8WOxbDvzmf7H5k2/7wmx89ZERdjGmRGLWpFFb94muM5QMp7Rw9A4P0DQ7RN5Smb3CI/qEhegeH6B9M0zfkH2df7x8coi/zuD9z/GDaMZhOM5BOMziUZjDtGEin6elP05EeZGAozWA683zm54Fhx43WHDbRhMwHxzsbTuGmt64o+PmDDIL5QOOwx0289Nv+aMfMB14UBGZ2DXANwKJFiwpeUJHJxKzwwzqHN8dkbyd6/OJ7A6I4Fz3B68d/Hl6TGe0ao7022uOs7PnSzjGUPn4bzNxnn3/hdecYHEozlIYhlybtYCidftHrLzp+xHO+HI40/t45SJO5zxQwe9wLx+NIj3g88vXjjx0Ni2aM/T/aOAQZBKN9Xxj5n2ssx+Ccuw24DfyoofyLJiLjkf32PzHbyY3RP2okK8j+9SZg4bDHC4D9ORwjIiIBCjIINgDLzGyJmcWAq4E7RxxzJ/Bu884Hjqp/QESkuAJrGnLODZrZdcA9+OGj33TObTOzazOvfw34KX7o6E788NH3BlUeEREZXaDzCJxzP8V/2A9/7mvDfnbAnwVZBhEROTnNwRMRqXAKAhGRCqcgEBGpcAoCEZEKN+GWoTazVmBPqctRQnVAW6kLUUKV/v5BfwO9/9ze/2Ln3MzRXphwQVDpzGzjidYUrwSV/v5BfwO9/8K/fzUNiYhUOAWBiEiFUxBMPLeVugAlVunvH/Q30PsvMPURiIhUONUIREQqnIJARKTCKQjKlJktNLNfmNnTZrbNzP4i8/x0M/u5mT2buZ9W6rIGyczCZrbZzH6ceVxp77/WzL5nZs9k/l+4oJL+Bmb2l5n//58ys++YWWKyv38z+6aZHTSzp4Y9d8L3bGYfN7OdZrbDzC7P5ZoKgvI1CPyVc+504Hzgz8xsJXA9cL9zbhlwf+bxZPYXwNPDHlfa+/8ScLdzbgWwGv+3qIi/gZnNBz4INDjnzsQvZ381k//9/xtwxYjnRn3Pmc+Eq4EzMr/zFTMb90alCoIy5Zxrds49nvm5A/8BMB+4CvhW5rBvAb9XkgIWgZktAF4PfH3Y05X0/qcAFwPfAHDO9TvnjlBBfwP8UvlJM4sAVfgdDCf1+3fO/QpoH/H0id7zVcDtzrk+59wu/N4u68Z7TQXBBGBm9cBa4FFgdnYXt8z9rBIWLWhfBP4aGLateUW9/1OAVuBfM81jXzezFBXyN3DO7QP+EdgLNON3MLyXCnn/I5zoPc8HGocd15R5blwUBGXOzKqB7wMfcs4dK3V5isXM3gAcdM5tKnVZSigCnA181Tm3Fuhi8jWDnFCmHfwqYAkwD0iZ2TtLW6qyY6M8N+45AQqCMmZmUXwI/Kdz7geZpw+Y2dzM63OBg6UqX8BeAbzJzHYDtwOvMbP/oHLeP/hvd03OuUczj7+HD4ZK+RtcCuxyzrU65waAHwAXUjnvf7gTvecmYOGw4xbgm8/GRUFQpszM8G3DTzvnvjDspTuB92R+fg9wR7HLVgzOuY875xY45+rxnWEPOOfeSYW8fwDnXAvQaGanZZ56LbCdyvkb7AXON7OqzL+H1+L7yirl/Q93ovd8J3C1mcXNbAmwDHhsvCfXzOIyZWYXAb8GnuR4G/kn8P0E3wUW4f+h/L5zbmTH0qRiZpcAH3HOvcHMZlBB79/M1uA7y2PA88B78V/gKuJvYGY3Af8LP4puM/AnQDWT+P2b2XeAS/DLTR8APg38iBO8ZzP7JPA+/N/oQ865n437mgoCEZHKpqYhEZEKpyAQEalwCgIRkQqnIBARqXAKAhGRCqcgEBGpcAoCEZEKpyAQGQcz+5GZbcqskX9N5rk/NrPfmdkvzexfzOzLmednmtn3zWxD5vaK0pZeZHSaUCYyDmY23TnXbmZJYANwOfAQfg2gDuAB4Ann3HVm9l/AV5xzvzGzRcA9mf0lRMpKpNQFEJlgPmhmb878vBB4F/DgsOn+/wMsz7x+KbDSL5MDwBQzq8nsLyFSNhQEImOUWfPoUuAC51y3mf0S2AGc6Ft+KHNsT1EKKJIj9RGIjN1U4HAmBFbgtxCtAl5lZtMyu2i9ddjx9wLXZR9kFpATKTsKApGxuxuImNlW4Gbgt8A+4HP4VWHvwy8TfTRz/AeBBjPbambbgWuLX2SRl6fOYpE8mVm1c64zUyP4IfBN59wPS10ukbFSjUAkf58xsy3AU8Au/NrxIhOGagQiIhVONQIRkQqnIBARqXAKAhGRCqcgEBGpcAoCEZEK9/8B2pPGOBQbqVcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# изобразим на графике зависимость вероятности того, что inlf = 1 от возраста женщины\n",
    "preds['age'] = new['age']\n",
    "sns.lineplot(data=preds, x='age', y='mean');\n",
    "plt.fill_between(preds['age'], preds['mean_ci_lower'], preds['mean_ci_upper'], color='blue', alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4739f4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
       "<Wald test (chi2): statistic=[[53.54026164]], p-value=2.3652655374994114e-12, df_denom=2>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# проверим гипотезу о том, что количество детей младше и старше 6 лет не влияет на вероятность того, что женщина будет работать\n",
    "# с помощью теста Вальда\n",
    "hypo = 'kidslt6 = 0, kidsge6 = 0'\n",
    "fit_logit.wald_test(hypo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c079fdc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Marginal Effects</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>  <td>inlf</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>dydx</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>At:</th>            <td>overall</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <th></th>        <th>dy/dx</th>    <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>educ</th>     <td>    0.0395</td> <td>    0.007</td> <td>    5.414</td> <td> 0.000</td> <td>    0.025</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exper</th>    <td>    0.0368</td> <td>    0.005</td> <td>    7.139</td> <td> 0.000</td> <td>    0.027</td> <td>    0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>expersq</th>  <td>   -0.0006</td> <td>    0.000</td> <td>   -3.176</td> <td> 0.001</td> <td>   -0.001</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>      <td>   -0.0157</td> <td>    0.002</td> <td>   -6.603</td> <td> 0.000</td> <td>   -0.020</td> <td>   -0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidslt6</th>  <td>   -0.2578</td> <td>    0.032</td> <td>   -8.070</td> <td> 0.000</td> <td>   -0.320</td> <td>   -0.195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidsge6</th>  <td>    0.0107</td> <td>    0.013</td> <td>    0.805</td> <td> 0.421</td> <td>   -0.015</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nwifeinc</th> <td>   -0.0038</td> <td>    0.001</td> <td>   -2.571</td> <td> 0.010</td> <td>   -0.007</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "        Logit Marginal Effects       \n",
       "=====================================\n",
       "Dep. Variable:                   inlf\n",
       "Method:                          dydx\n",
       "At:                           overall\n",
       "==============================================================================\n",
       "                dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "educ           0.0395      0.007      5.414      0.000       0.025       0.054\n",
       "exper          0.0368      0.005      7.139      0.000       0.027       0.047\n",
       "expersq       -0.0006      0.000     -3.176      0.001      -0.001      -0.000\n",
       "age           -0.0157      0.002     -6.603      0.000      -0.020      -0.011\n",
       "kidslt6       -0.2578      0.032     -8.070      0.000      -0.320      -0.195\n",
       "kidsge6        0.0107      0.013      0.805      0.421      -0.015       0.037\n",
       "nwifeinc      -0.0038      0.001     -2.571      0.010      -0.007      -0.001\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# рассчитаем средние предельные эффекты\n",
    "fit_logit.get_margeff(at='overall').summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b2dacbcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Marginal Effects</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th> <td>inlf</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>dydx</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>At:</th>            <td>mean</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <th></th>        <th>dy/dx</th>    <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>educ</th>     <td>    0.0538</td> <td>    0.011</td> <td>    5.092</td> <td> 0.000</td> <td>    0.033</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exper</th>    <td>    0.0501</td> <td>    0.008</td> <td>    6.397</td> <td> 0.000</td> <td>    0.035</td> <td>    0.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>expersq</th>  <td>   -0.0008</td> <td>    0.000</td> <td>   -3.096</td> <td> 0.002</td> <td>   -0.001</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>      <td>   -0.0214</td> <td>    0.004</td> <td>   -6.046</td> <td> 0.000</td> <td>   -0.028</td> <td>   -0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidslt6</th>  <td>   -0.3509</td> <td>    0.050</td> <td>   -7.070</td> <td> 0.000</td> <td>   -0.448</td> <td>   -0.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kidsge6</th>  <td>    0.0146</td> <td>    0.018</td> <td>    0.804</td> <td> 0.422</td> <td>   -0.021</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nwifeinc</th> <td>   -0.0052</td> <td>    0.002</td> <td>   -2.534</td> <td> 0.011</td> <td>   -0.009</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "        Logit Marginal Effects       \n",
       "=====================================\n",
       "Dep. Variable:                   inlf\n",
       "Method:                          dydx\n",
       "At:                              mean\n",
       "==============================================================================\n",
       "                dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "educ           0.0538      0.011      5.092      0.000       0.033       0.074\n",
       "exper          0.0501      0.008      6.397      0.000       0.035       0.065\n",
       "expersq       -0.0008      0.000     -3.096      0.002      -0.001      -0.000\n",
       "age           -0.0214      0.004     -6.046      0.000      -0.028      -0.014\n",
       "kidslt6       -0.3509      0.050     -7.070      0.000      -0.448      -0.254\n",
       "kidsge6        0.0146      0.018      0.804      0.422      -0.021       0.050\n",
       "nwifeinc      -0.0052      0.002     -2.534      0.011      -0.009      -0.001\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# рассчитаем предельные эффекты для \"среднего\" наблюдения\n",
    "fit_logit.get_margeff(at='mean').summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "80236390",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = t[['educ', 'exper', 'expersq', 'age', 'kidslt6', 'kidsge6', 'nwifeinc']]\n",
    "y = t['inlf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "86f46abe",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a432eac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sk_logit = LogisticRegression(penalty='none',  max_iter=1000).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e1dcde05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.23369565,  0.24266178, -0.00431764, -0.09684276, -1.51987061,\n",
       "         0.05835955, -0.01972794]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_logit.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "18f45697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.51347541])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_logit.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9311a541",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_pred = sk_logit.predict_proba(X_test)\n",
    "prob_pred = prob_pred[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "85644e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.76846809, 0.94470067, 0.93133593, 0.62346108, 0.23504335,\n",
       "       0.91752442, 0.91719988, 0.8320266 , 0.70456353, 0.92292415,\n",
       "       0.52103883, 0.87106524, 0.82301592, 0.59772907, 0.23540533,\n",
       "       0.18646745, 0.77104492, 0.20636924, 0.76633065, 0.30848067,\n",
       "       0.70399737, 0.21367654, 0.07091306, 0.9600996 , 0.62846069,\n",
       "       0.44327151, 0.8652395 , 0.59544058, 0.63079118, 0.79112589,\n",
       "       0.14233592, 0.40377942, 0.04857995, 0.54618881, 0.84296143,\n",
       "       0.96068848, 0.58254983, 0.91479579, 0.89919922, 0.87537608,\n",
       "       0.93050729, 0.06105931, 0.30323255, 0.39983122, 0.66385527,\n",
       "       0.92986448, 0.77051853, 0.43605986, 0.9671568 , 0.49361907,\n",
       "       0.69749997, 0.69828266, 0.91099989, 0.79430692, 0.13411641,\n",
       "       0.80281808, 0.68643284, 0.82787002, 0.75606514, 0.67800484,\n",
       "       0.73842717, 0.65101702, 0.80518468, 0.93835694, 0.93188958,\n",
       "       0.47639261, 0.11694663, 0.21644067, 0.95220381, 0.57738051,\n",
       "       0.19178104, 0.71297022, 0.40005533, 0.50553914, 0.69248966,\n",
       "       0.5828267 , 0.50180962, 0.95455108, 0.6596453 , 0.87124156,\n",
       "       0.71424503, 0.79544837, 0.19585277, 0.38613469, 0.38235449,\n",
       "       0.13745135, 0.74937567, 0.53354383, 0.46999888, 0.79533136,\n",
       "       0.58337911, 0.86935221, 0.95110009, 0.82923666, 0.76072224,\n",
       "       0.83008802, 0.37365101, 0.80415712, 0.42136606, 0.71612853,\n",
       "       0.81315908, 0.22539217, 0.21960708, 0.89617728, 0.60380931,\n",
       "       0.69051262, 0.90379794, 0.47069609, 0.51322457, 0.77735248,\n",
       "       0.69231684, 0.26275816, 0.5091893 , 0.29857015, 0.77728824,\n",
       "       0.35683339, 0.47911823, 0.79744428, 0.56003706, 0.76847876,\n",
       "       0.6631279 , 0.85604706, 0.68369616, 0.91561165, 0.55465512,\n",
       "       0.8529423 , 0.36066832, 0.91681093, 0.50413843, 0.88936293,\n",
       "       0.70689746, 0.16071967, 0.74774585, 0.18029831, 0.37907669,\n",
       "       0.09993676, 0.92523967, 0.04111864, 0.87910363, 0.62191845,\n",
       "       0.7089504 , 0.30148856, 0.90918885, 0.14981664, 0.76387512,\n",
       "       0.90444573, 0.90471913, 0.14858268, 0.20610904, 0.22931657,\n",
       "       0.28788364])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2b4bb681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50, 17],\n",
       "       [31, 53]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, prob_pred > 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "abd66d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[38, 29],\n",
       "       [21, 63]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, prob_pred > 0.58)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "56979351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# матрица сопряженности\n",
    "sns.heatmap(confusion_matrix(y_test, prob_pred > 0.7), annot=True, fmt='d', cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2152e9fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# построим ROC-кривую\n",
    "# x: FPR = FP / (FP + TN) = FP / condition Negative\n",
    "# Специфичность = 1 - FPR\n",
    "# y: TPR = TP / (TP + FN) = TP / condition Positive\n",
    "# Чувствительность = TPR\n",
    "plot_roc_curve(sk_logit, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8a7cad8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_precision_recall_curve(sk_logit, X_test, y_test);\n",
    "\n",
    "# Recall = TPR = TP / condition Positive\n",
    "# precision = TP / predicted Positive"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}