IG_Decision_trees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47af795a-7642-4b9c-8b55-d82f229ed649",
   "metadata": {},
   "source": [
    "# **Задание** \n",
    "\n",
    "## **Цель:** изучить применение дерева решений в рамках задачи регрессии \n",
    "### **Описание задания:** \n",
    "В домашнем задании нужно решить задачу регрессии. В качестве датасета необходимо взять данные о недвижимости Калифорнии из библиотеки [sklearn.datasets](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html). Целевая переменная – MedHouseVal. Прочитать информацию о признаках датасета можно, выполнив следующий код – print(fetch_california_housing().DESCR). На полученных данных построить модель регрессии и дерево решений. \n",
    "  \n",
    "Для получения зачета по этому домашнему заданию, должно быть как минимум реализовано обучение двух моделей, выведена их точность, оптимизирован один параметр дерева решений.  \n",
    "  \n",
    "**Результат:** получены знания по работе с деревом решений\n",
    "\n",
    "### **Этапы работы:** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293763b2-9acd-4128-931d-dc04aba69829",
   "metadata": {},
   "source": [
    "1. Получите данные и загрузите их в рабочую среду. (Jupyter Notebook или другую)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b803009c-a13a-4320-82b7-3fa30ff48ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.tree import DecisionTreeRegressor, plot_tree\n",
    "from sklearn import preprocessing\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c43c5ff-071f-4058-ab49-1f89044b009e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.pipeline import make_pipeline\n",
    "#from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "324b3995-c4c2-4f93-a903-f504ce2b8b31",
   "metadata": {
    "id": "qlEjVLBEW1Lg"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score, mean_squared_error, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30648635-c5df-49d3-a2d6-d26593be089b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "635f5180-db32-40da-b0ed-a4a96daff0d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9af531-9781-4ef6-88d6-a00e25e84c51",
   "metadata": {},
   "source": [
    "    Attribute Information:\n",
    "        - MedInc        median income in block group\n",
    "        - HouseAge      median house age in block group\n",
    "        - AveRooms      average number of rooms per household\n",
    "        - AveBedrms     average number of bedrooms per household\n",
    "        - Population    block group population\n",
    "        - AveOccup      average number of household members\n",
    "        - Latitude      block group latitude\n",
    "        - Longitude     block group longitude\n",
    "    :Missing Attribute Values: None\n",
    "\n",
    "The target variable is the median house value for California districts,\n",
    "expressed in hundreds of thousands of dollars ($100,000).\n",
    "\n",
    "A household is a group of people residing within a home. Since the average\n",
    "number of rooms and bedrooms in this dataset are provided per household, these\n",
    "columns may take surprisingly large values for block groups with few households\n",
    "and many empty houses, such as vacation resorts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edd297ee-f042-4a0d-b3d8-af83f1ab00db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6217cdc3-6931-4560-aa94-c98e1308842b",
   "metadata": {},
   "source": [
    "2. Проведите первичный анализ.  \n",
    "   a. Проверьте данные на пропуски. Удалите в случае обнаружения.  \n",
    "   b. *Нормализуйте один из признаков.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00480c3e-d3dc-4e70-bd1c-2bf376bcca4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc        0\n",
       "HouseAge      0\n",
       "AveRooms      0\n",
       "AveBedrms     0\n",
       "Population    0\n",
       "AveOccup      0\n",
       "Latitude      0\n",
       "Longitude     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49fc2246-c6b8-4293-a3e4-12386fcb0cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9bb37653-a59f-4bdf-8895-9c4b00a12490",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
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   "cell_type": "code",
   "execution_count": 9,
   "id": "502a0f7c-d69b-4647-9db5-3b6b81c1e58b",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.870671</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>5.429000</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>3.070655</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>-119.569704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.899822</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2.474173</td>\n",
       "      <td>0.473911</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>10.386050</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>2.003532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>-124.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.563400</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.440716</td>\n",
       "      <td>1.006079</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>2.429741</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>-121.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.534800</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.229129</td>\n",
       "      <td>1.048780</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>2.818116</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>-118.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.743250</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.052381</td>\n",
       "      <td>1.099526</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>3.282261</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>-118.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-114.310000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
       "\n",
       "           AveOccup      Latitude     Longitude  \n",
       "count  20640.000000  20640.000000  20640.000000  \n",
       "mean       3.070655     35.631861   -119.569704  \n",
       "std       10.386050      2.135952      2.003532  \n",
       "min        0.692308     32.540000   -124.350000  \n",
       "25%        2.429741     33.930000   -121.800000  \n",
       "50%        2.818116     34.260000   -118.490000  \n",
       "75%        3.282261     37.710000   -118.010000  \n",
       "max     1243.333333     41.950000   -114.310000  "
      ]
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     "metadata": {},
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   "source": [
    "X.describe()"
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   "execution_count": 10,
   "id": "6d31384f-b4b4-4b0a-a45d-eac259f5df44",
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    {
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       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
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       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.870671</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>5.429000</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>3.070655</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>-119.569704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.899822</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2.474173</td>\n",
       "      <td>0.473911</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>10.386050</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>2.003532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>-124.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.563400</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.440716</td>\n",
       "      <td>1.006079</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>2.429741</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>-121.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.534800</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.229129</td>\n",
       "      <td>1.048780</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>2.818116</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>-118.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.743250</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.052381</td>\n",
       "      <td>1.099526</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>3.282261</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>-118.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-114.310000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
       "\n",
       "           AveOccup      Latitude     Longitude  \n",
       "count  20640.000000  20640.000000  20640.000000  \n",
       "mean       3.070655     35.631861   -119.569704  \n",
       "std       10.386050      2.135952      2.003532  \n",
       "min        0.692308     32.540000   -124.350000  \n",
       "25%        2.429741     33.930000   -121.800000  \n",
       "50%        2.818116     34.260000   -118.490000  \n",
       "75%        3.282261     37.710000   -118.010000  \n",
       "max     1243.333333     41.950000   -114.310000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d008c0d2-e4b4-4a59-a28f-d8aaea9b1cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ec9de13-76e2-428e-9747-7c8fc7292d92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mms.fit(np.array([X['AveOccup']]).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c798647f-eba6-4628-98ee-55e25fe6aba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X['AveOccup'] = pd.DataFrame(mms.transform(np.array([X['AveOccup']]).T)) \n",
    "# нормализовали признак с наиболее большим разбросом значений и большими значениями\n",
    "\n",
    "## X['AveRooms'] = pd.DataFrame(preprocessing.normalize([np.array(X['AveRooms'])]).T) # другой способ нормализации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "017cd308-216e-40c4-a672-fccff3e24b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  0.001499     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  0.001141     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  0.001698     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  0.001493     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  0.001198     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2048c99-c7fa-4b5e-a217-75889d6f98e1",
   "metadata": {},
   "source": [
    "3. Разделите выборку на обучающее и тестовое подмножества. 80% данных оставить на обучающее множество, 20% - на тестовое."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e78721a5-85e2-4b55-bd77-aafabd899d92",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26aa4226-0b77-4b2a-9883-0dfebf925e1a",
   "metadata": {},
   "source": [
    "4. Обучите [модель регрессии](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) на обучающем множестве."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4b0be05-983b-489e-ad5b-5f10e03748ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model= LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56fd24be-1f4a-4ae8-8d68-28bf35959c79",
   "metadata": {},
   "source": [
    "5. Для тестового множества предскажите целевую переменную и сравните с истинным значением, посчитав точность предсказания модели. Для этого используйте встроенную функцию score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "380a6b23-b78a-48b3-b055-6d3a1b454ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "27f2c039-7db2-420f-b006-751a57dc0a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.588233669113431"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24a01d64-f85c-47c7-ac5b-6f596c0fecad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e56486a9-5338-4895-b882-7ae1abc85eb7",
   "metadata": {},
   "source": [
    "6. Обучите [дерево решений](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) на обучающем множестве.  \n",
    "a. Повторите п. 5 для полученной модели.  \n",
    "b. Визуализируйте часть дерева решений. Убедитесь, что график получился читабельным. Посмотрите примеры визуализации по [ссылке](https://mljar.com/blog/visualize-decision-tree/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2df0b8fa-5ef1-4240-a001-30c40dc13e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=4, random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=4, random_state=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=4, random_state=10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_trees = DecisionTreeRegressor(max_depth=4, random_state=10)\n",
    "model_trees.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d686cbea-7b9f-4290-a157-cbf9f04e9ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model_trees.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b75fe37e-4103-420d-9607-4fe50e02eb0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.556200496265622"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_trees.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8d3f344b-bfc3-4d8a-a44e-3e946f9ac796",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2400x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 8), dpi=80)\n",
    "plot_tree(model_trees, feature_names=X.columns.to_list(), filled=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32681210-c123-403f-ac81-39728151fded",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "942a6fba-ea66-43bb-bcb9-1f1db0f974b1",
   "metadata": {},
   "source": [
    "7. Оптимизируйте глубину дерева (max_depth). *Оптимизируйте ещё один параметр модели на выбор.  \n",
    "a. Повторите п. 5 для полученной модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3b46341c-49a3-4297-a0c2-44a05043a87d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Узнаём значение R2 при отсутствии ограничения на количество глубины деревьев\n",
    "model_trees = DecisionTreeRegressor(random_state=10)\n",
    "model_trees.fit(X_train, y_train)\n",
    "y_test_pred = model_trees.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "81705231-6af8-4909-8cc9-24cdd36ec1d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5998076922239675"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_trees.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "352c2b2a-e2f3-4997-a21c-53143203b10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_trees.get_depth() # максимальная гглубина, которую используем как верхнюю границу для поиска оптимальной глубины"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d58b3fa3-837a-4a06-8c6d-1980d439265a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# расчёт точности при разной глубине:\n",
    "max_depth_range = list(range(1, 35))\n",
    "accuracy = [] \n",
    "for depth in max_depth_range:\n",
    "    reg = DecisionTreeRegressor(max_depth = depth, random_state = 0)\n",
    "    reg.fit(X_train, y_train)    \n",
    "    score = reg.score(X_test, y_test)\n",
    "    accuracy.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "811f3119-d72f-43ec-a67f-843dae106afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x,i=[],1\n",
    "while i< len(accuracy)+1:\n",
    "    x.append(i)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ddb4e095-0b6e-47b3-9010-172a4ec8bd28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 4), dpi=80)\n",
    "plt.grid(color='k', linestyle='--', linewidth=0.5)\n",
    "plt.xlabel('max_depth')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlim(1, 35)\n",
    "plt.ylim(0.28, 0.7)\n",
    "plt.xticks(np.arange(0,35,1))\n",
    "plt.title('Accuracy vs. max_depth')\n",
    "plt.plot(x, accuracy)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6789ed5c-48bb-4d95-9889-91495c005887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6876584386825477"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(accuracy) # достигается при оптимальном выборе max_depth = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f5976f3-ae0d-4f78-a39a-c29468d827f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7692046446282716"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим на точность на обучающейся выборке и сравним с точностью на тренировочной выборке\n",
    "model_trees = DecisionTreeRegressor(random_state=10, max_depth=8)\n",
    "model_trees.fit(X_train, y_train)\n",
    "model_trees.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe73880-96c5-44d9-bd2d-f9534a76b87b",
   "metadata": {},
   "source": [
    "##### Вывод: Даже на оптимальной точности по тестовой выборке модель выглядит переобучившейся."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3adf030a-f6b4-49b4-8e1a-bd1396e5647f",
   "metadata": {},
   "source": [
    "8. Сформулируйте выводы по проделанной работе.  \n",
    "a. Сравните точность двух моделей.  \n",
    "b. Напишите свое мнение, для каких задач предпочтительнее использовать обученные в работе модели? Какие у них есть плюсы и минусы?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8563c14-a676-4562-b7a7-0c41ad874f21",
   "metadata": {},
   "source": [
    "### **Выводы:**  \n",
    "а. В данном случае точность при использовании Деревьев выше чем при использовании обычной Линейной регрессии, но в данном случае модель выглядит переобучившейся, поскольку точность на тренировочной выборке значительно выше чем на тестовой.  \n",
    "б. Деревья, по-видимому, лучше работают, когда зависимость целевой переменной от других переменных нелинейная."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a43b1db-4e80-4b8b-8ada-6d0ef61d2b7e",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "91c87d27-661b-44b5-819f-134f2d259e9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f106d28-3782-4fdd-b344-be29c9334132",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80bdc863-887c-4e5b-8061-d610f608ee66",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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