IG_Decision_trees_v2.ipynb

{
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  {
   "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 sklearn.preprocessing import MinMaxScaler\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.pipeline import make_pipeline\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.  "
   ]
  },
  {
   "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",
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       "      <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "   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"
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   "source": [
    "X.head()"
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   "cell_type": "code",
   "execution_count": 9,
   "id": "502a0f7c-d69b-4647-9db5-3b6b81c1e58b",
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    {
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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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       "  </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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       "             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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     "execution_count": 9,
     "metadata": {},
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    "X.describe()"
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   "execution_count": 10,
   "id": "017cd308-216e-40c4-a672-fccff3e24b19",
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    {
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       "      <th>MedInc</th>\n",
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       "      <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",
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       "      <td>-122.23</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2048c99-c7fa-4b5e-a217-75889d6f98e1",
   "metadata": {},
   "source": [
    "3. Провести нормализацию.  \n",
    "   Разделите выборку на обучающее и тестовое подмножества. 80% данных оставить на обучающее множество, 20% - на тестовое."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f88f4bfe-052b-4932-b02a-f0e5a59b1be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "mms = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb82df1d-7652-4443-8633-dc0860c9092a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([X, y], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc16d0fb-14da-4c38-abd1-b03dd76dc691",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "mms.fit(df)"
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d23736e6-33d7-4c2e-b456-61ebdd6c8a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(mms.transform(df), columns=df.columns.to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "789317a9-d967-44c6-a6c6-e5c473091bdd",
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.539668</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.043512</td>\n",
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       "      <td>0.212151</td>\n",
       "      <td>0.708247</td>\n",
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       "      <td>0.001493</td>\n",
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       "      <td>0.209163</td>\n",
       "      <td>0.672783</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.022166</td>\n",
       "      <td>0.015752</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>0.209163</td>\n",
       "      <td>0.674638</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>0.073130</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.029769</td>\n",
       "      <td>0.023715</td>\n",
       "      <td>0.023599</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>0.737513</td>\n",
       "      <td>0.324701</td>\n",
       "      <td>0.130105</td>\n",
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       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>0.141853</td>\n",
       "      <td>0.333333</td>\n",
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       "      <td>0.009894</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.738576</td>\n",
       "      <td>0.312749</td>\n",
       "      <td>0.128043</td>\n",
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       "    <tr>\n",
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       "      <td>0.028140</td>\n",
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       "      <td>0.311753</td>\n",
       "      <td>0.159383</td>\n",
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       "    <tr>\n",
       "      <th>20638</th>\n",
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       "      <td>0.732200</td>\n",
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       "         MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  \\\n",
       "0      0.539668  0.784314  0.043512   0.020469    0.008941  0.001499   \n",
       "1      0.538027  0.392157  0.038224   0.018929    0.067210  0.001141   \n",
       "2      0.466028  1.000000  0.052756   0.021940    0.013818  0.001698   \n",
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       "4      0.230776  1.000000  0.038534   0.022166    0.015752  0.001198   \n",
       "...         ...       ...       ...        ...         ...       ...   \n",
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       "20636  0.141853  0.333333  0.037344   0.029124    0.009894  0.001956   \n",
       "20637  0.082764  0.313725  0.030904   0.023323    0.028140  0.001314   \n",
       "20638  0.094295  0.333333  0.031783   0.024859    0.020684  0.001152   \n",
       "20639  0.130253  0.294118  0.031252   0.024573    0.038790  0.001549   \n",
       "\n",
       "       Latitude  Longitude  MedHouseVal  \n",
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       "2      0.564293   0.210159     0.695051  \n",
       "3      0.564293   0.209163     0.672783  \n",
       "4      0.564293   0.209163     0.674638  \n",
       "...         ...        ...          ...  \n",
       "20635  0.737513   0.324701     0.130105  \n",
       "20636  0.738576   0.312749     0.128043  \n",
       "20637  0.732200   0.311753     0.159383  \n",
       "20638  0.732200   0.301793     0.143713  \n",
       "20639  0.725824   0.309761     0.153403  \n",
       "\n",
       "[20640 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c9cabe3d-f7b2-4cff-86aa-2ab539e774c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2cd11eb9-dfe7-4c73-8ebd-258352ca0ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.iloc[:,8:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "id": "b4b0be05-983b-489e-ad5b-5f10e03748ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
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     "data": {
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       "<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": 19,
     "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": 20,
   "id": "380a6b23-b78a-48b3-b055-6d3a1b454ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2af4372e-7b0c-4eaa-9fa7-022ee21482f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.610725161317683"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "27f2c039-7db2-420f-b006-751a57dc0a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5882336691134314"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "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": 23,
     "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": 24,
   "id": "d686cbea-7b9f-4290-a157-cbf9f04e9ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model_trees.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d4760aac-0da1-4573-94f4-be05ddb048be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5910113461747724"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_trees.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b75fe37e-4103-420d-9607-4fe50e02eb0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5562004962656217"
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     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_trees.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8d3f344b-bfc3-4d8a-a44e-3e946f9ac796",
   "metadata": {},
   "outputs": [
    {
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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": 28,
   "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": 29,
   "id": "81705231-6af8-4909-8cc9-24cdd36ec1d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6005956216311379"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_trees.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "352c2b2a-e2f3-4997-a21c-53143203b10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "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": 32,
   "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": 33,
   "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(0.2, 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": 34,
   "id": "6789ed5c-48bb-4d95-9889-91495c005887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.683952953701713"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(accuracy) # достигается при оптимальном выборе max_depth = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8f5976f3-ae0d-4f78-a39a-c29468d827f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7692046446282715"
      ]
     },
     "execution_count": 35,
     "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": []
  },
  {
   "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": []
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}