From 87ea90c489ffd68aa073426e419fcaba06e9001d Mon Sep 17 00:00:00 2001
From: shlear <116897538+shlear@users.noreply.github.com>
Date: Mon, 30 Jan 2023 04:59:38 +0300
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---
 Ensembles_HW.ipynb | 888 +++++++++++++++++++++++++++++++++++++++++++++
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diff --git a/Ensembles_HW.ipynb b/Ensembles_HW.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyOiiwhPTrOemDpm4G8qq5FH",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/shlear/MLDM-2022/blob/main/Ensembles_HW.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 380
+        },
+        "id": "fBoF1iTNtXl-",
+        "outputId": "6731c3f8-5846-4597-da73-409f9a88a880"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "20640\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "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",
+              "5  4.0368      52.0  4.761658   1.103627       413.0  2.139896     37.85   \n",
+              "6  3.6591      52.0  4.931907   0.951362      1094.0  2.128405     37.84   \n",
+              "7  3.1200      52.0  4.797527   1.061824      1157.0  1.788253     37.84   \n",
+              "8  2.0804      42.0  4.294118   1.117647      1206.0  2.026891     37.84   \n",
+              "9  3.6912      52.0  4.970588   0.990196      1551.0  2.172269     37.84   \n",
+              "\n",
+              "   Longitude  target  \n",
+              "0    -122.23   4.526  \n",
+              "1    -122.22   3.585  \n",
+              "2    -122.24   3.521  \n",
+              "3    -122.25   3.413  \n",
+              "4    -122.25   3.422  \n",
+              "5    -122.25   2.697  \n",
+              "6    -122.25   2.992  \n",
+              "7    -122.25   2.414  \n",
+              "8    -122.26   2.267  \n",
+              "9    -122.25   2.611  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5b335d61-9b07-4a69-9703-65a1135f2bd7\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "<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",
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+              "  </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>2.555556</td>\n",
+              "      <td>37.88</td>\n",
+              "      <td>-122.23</td>\n",
+              "      <td>4.526</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",
+              "      <td>3.585</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",
+              "      <td>3.521</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",
+              "      <td>3.413</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",
+              "      <td>3.422</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4.0368</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>4.761658</td>\n",
+              "      <td>1.103627</td>\n",
+              "      <td>413.0</td>\n",
+              "      <td>2.139896</td>\n",
+              "      <td>37.85</td>\n",
+              "      <td>-122.25</td>\n",
+              "      <td>2.697</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>3.6591</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>4.931907</td>\n",
+              "      <td>0.951362</td>\n",
+              "      <td>1094.0</td>\n",
+              "      <td>2.128405</td>\n",
+              "      <td>37.84</td>\n",
+              "      <td>-122.25</td>\n",
+              "      <td>2.992</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>3.1200</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>4.797527</td>\n",
+              "      <td>1.061824</td>\n",
+              "      <td>1157.0</td>\n",
+              "      <td>1.788253</td>\n",
+              "      <td>37.84</td>\n",
+              "      <td>-122.25</td>\n",
+              "      <td>2.414</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>2.0804</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>4.294118</td>\n",
+              "      <td>1.117647</td>\n",
+              "      <td>1206.0</td>\n",
+              "      <td>2.026891</td>\n",
+              "      <td>37.84</td>\n",
+              "      <td>-122.26</td>\n",
+              "      <td>2.267</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>3.6912</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>4.970588</td>\n",
+              "      <td>0.990196</td>\n",
+              "      <td>1551.0</td>\n",
+              "      <td>2.172269</td>\n",
+              "      <td>37.84</td>\n",
+              "      <td>-122.25</td>\n",
+              "      <td>2.611</td>\n",
+              "    </tr>\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
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+              "\n",
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+              "\n",
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+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
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+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "from sklearn.datasets import fetch_california_housing\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "\n",
+        "dataset = fetch_california_housing()\n",
+        "\n",
+        "data = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
+        "data['target'] = dataset.target\n",
+        "\n",
+        "print(len(data))\n",
+        "data.head(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(dataset.DESCR)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cM3qC68rthTz",
+        "outputId": "a54919fa-53f3-4433-d8c3-7d236256b65d"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            ".. _california_housing_dataset:\n",
+            "\n",
+            "California Housing dataset\n",
+            "--------------------------\n",
+            "\n",
+            "**Data Set Characteristics:**\n",
+            "\n",
+            "    :Number of Instances: 20640\n",
+            "\n",
+            "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
+            "\n",
+            "    :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",
+            "\n",
+            "    :Missing Attribute Values: None\n",
+            "\n",
+            "This dataset was obtained from the StatLib repository.\n",
+            "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\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",
+            "This dataset was derived from the 1990 U.S. census, using one row per census\n",
+            "block group. A block group is the smallest geographical unit for which the U.S.\n",
+            "Census Bureau publishes sample data (a block group typically has a population\n",
+            "of 600 to 3,000 people).\n",
+            "\n",
+            "An 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 surpinsingly large values for block groups with few households\n",
+            "and many empty houses, such as vacation resorts.\n",
+            "\n",
+            "It can be downloaded/loaded using the\n",
+            ":func:`sklearn.datasets.fetch_california_housing` function.\n",
+            "\n",
+            ".. topic:: References\n",
+            "\n",
+            "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
+            "      Statistics and Probability Letters, 33 (1997) 291-297\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(5, 4), dpi=100)\n",
+        "plt.scatter(data.Longitude, data.Latitude, s=data.target, c=data.target, cmap='bwr');"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 362
+        },
+        "id": "-F6iKPL5tklF",
+        "outputId": "77822afa-6439-48b2-f4aa-288dab2d8abb"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X, y = data.drop('target', axis=1), data['target']\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1234)\n",
+        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5ZdeUU1qtm3D",
+        "outputId": "7f122056-8b52-4033-b8ed-e6a39b3380bd"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(15480, 8) (5160, 8) (15480,) (5160,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.compose import make_column_transformer\n",
+        "from sklearn.pipeline import make_pipeline\n",
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "from sklearn.ensemble import RandomForestRegressor, StackingRegressor\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "from sklearn.metrics import mean_squared_error"
+      ],
+      "metadata": {
+        "id": "GchZnidctpBW"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1"
+      ],
+      "metadata": {
+        "id": "iM85IHb7tw7J"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "knn = KNeighborsRegressor()\n",
+        "knn.fit(X_train[[\"Longitude\", \"Latitude\"]], y_train)\n",
+        "print(\"knn_train_score: \" + str(knn.score(X_train[[\"Longitude\", \"Latitude\"]], y_train)))\n",
+        "print(\"knn_test_score: \" + str(knn.score(X_test[[\"Longitude\", \"Latitude\"]], y_test)))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jIkzDLB6trPm",
+        "outputId": "27833942-ed3d-404a-ebf6-719f66e3dbe1"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "knn_train_score: 0.865832069541342\n",
+            "knn_test_score: 0.7765334830390247\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2"
+      ],
+      "metadata": {
+        "id": "J_wVNwsctzKN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rf = RandomForestRegressor(random_state=42)\n",
+        "rf.fit(X_train.drop([\"Longitude\", \"Latitude\"], axis=1), y_train)\n",
+        "print(\"rf_train_score: \" + str(rf.score(X_train.drop([\"Longitude\", \"Latitude\"], axis=1), y_train))) # doubting overfitting\n",
+        "print(\"rf_test_score: \" + str(rf.score(X_test.drop([\"Longitude\", \"Latitude\"], axis=1), y_test)))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GjmBoM81ts-H",
+        "outputId": "2a2a392d-bfc0-4f27-b5d9-c0ebe3a66c5e"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "rf_train_score: 0.9563188385294168\n",
+            "rf_test_score: 0.6769816449039562\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3"
+      ],
+      "metadata": {
+        "id": "GFjpIsDPt5FA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "knn_pipeline = make_pipeline(make_column_transformer((\"passthrough\", [\"Longitude\", \"Latitude\"])), knn)\n",
+        "rf_pipeline = make_pipeline(make_column_transformer((\"passthrough\", [\"MedInc\", \"HouseAge\", \"AveRooms\", \"AveBedrms\", \"Population\", \"AveOccup\"])), rf)\n",
+        "estimators = [\n",
+        "    ('kNN', knn_pipeline),\n",
+        "    ('random_forest', rf_pipeline)\n",
+        "]\n",
+        "clf = StackingRegressor(\n",
+        "    estimators=estimators, final_estimator=LinearRegression(), cv=5\n",
+        ")\n",
+        "clf"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z2aG2Mfot43s",
+        "outputId": "abe44896-7deb-4e04-a021-81bb9f330334"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "StackingRegressor(cv=5,\n",
+              "                  estimators=[('kNN',\n",
+              "                               Pipeline(steps=[('columntransformer',\n",
+              "                                                ColumnTransformer(transformers=[('passthrough',\n",
+              "                                                                                 'passthrough',\n",
+              "                                                                                 ['Longitude',\n",
+              "                                                                                  'Latitude'])])),\n",
+              "                                               ('kneighborsregressor',\n",
+              "                                                KNeighborsRegressor())])),\n",
+              "                              ('random_forest',\n",
+              "                               Pipeline(steps=[('columntransformer',\n",
+              "                                                ColumnTransformer(transformers=[('passthrough',\n",
+              "                                                                                 'passthrough',\n",
+              "                                                                                 ['MedInc',\n",
+              "                                                                                  'HouseAge',\n",
+              "                                                                                  'AveRooms',\n",
+              "                                                                                  'AveBedrms',\n",
+              "                                                                                  'Population',\n",
+              "                                                                                  'AveOccup'])])),\n",
+              "                                               ('randomforestregressor',\n",
+              "                                                RandomForestRegressor(random_state=42))]))],\n",
+              "                  final_estimator=LinearRegression())"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clf.get_params().keys()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dJLXKsnMt9mr",
+        "outputId": "bf307173-e090-44fa-e5d3-d12657da3e7d"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_keys(['cv', 'estimators', 'final_estimator__copy_X', 'final_estimator__fit_intercept', 'final_estimator__n_jobs', 'final_estimator__normalize', 'final_estimator__positive', 'final_estimator', 'n_jobs', 'passthrough', 'verbose', 'kNN', 'random_forest', 'kNN__memory', 'kNN__steps', 'kNN__verbose', 'kNN__columntransformer', 'kNN__kneighborsregressor', 'kNN__columntransformer__n_jobs', 'kNN__columntransformer__remainder', 'kNN__columntransformer__sparse_threshold', 'kNN__columntransformer__transformer_weights', 'kNN__columntransformer__transformers', 'kNN__columntransformer__verbose', 'kNN__columntransformer__verbose_feature_names_out', 'kNN__columntransformer__passthrough', 'kNN__kneighborsregressor__algorithm', 'kNN__kneighborsregressor__leaf_size', 'kNN__kneighborsregressor__metric', 'kNN__kneighborsregressor__metric_params', 'kNN__kneighborsregressor__n_jobs', 'kNN__kneighborsregressor__n_neighbors', 'kNN__kneighborsregressor__p', 'kNN__kneighborsregressor__weights', 'random_forest__memory', 'random_forest__steps', 'random_forest__verbose', 'random_forest__columntransformer', 'random_forest__randomforestregressor', 'random_forest__columntransformer__n_jobs', 'random_forest__columntransformer__remainder', 'random_forest__columntransformer__sparse_threshold', 'random_forest__columntransformer__transformer_weights', 'random_forest__columntransformer__transformers', 'random_forest__columntransformer__verbose', 'random_forest__columntransformer__verbose_feature_names_out', 'random_forest__columntransformer__passthrough', 'random_forest__randomforestregressor__bootstrap', 'random_forest__randomforestregressor__ccp_alpha', 'random_forest__randomforestregressor__criterion', 'random_forest__randomforestregressor__max_depth', 'random_forest__randomforestregressor__max_features', 'random_forest__randomforestregressor__max_leaf_nodes', 'random_forest__randomforestregressor__max_samples', 'random_forest__randomforestregressor__min_impurity_decrease', 'random_forest__randomforestregressor__min_samples_leaf', 'random_forest__randomforestregressor__min_samples_split', 'random_forest__randomforestregressor__min_weight_fraction_leaf', 'random_forest__randomforestregressor__n_estimators', 'random_forest__randomforestregressor__n_jobs', 'random_forest__randomforestregressor__oob_score', 'random_forest__randomforestregressor__random_state', 'random_forest__randomforestregressor__verbose', 'random_forest__randomforestregressor__warm_start'])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import GridSearchCV\n",
+        "param_grid = dict(\n",
+        "        kNN__kneighborsregressor__n_neighbors=[3, 5, 10, 20],\n",
+        "        random_forest__randomforestregressor__min_samples_leaf=[2, 4],\n",
+        "        random_forest__randomforestregressor__max_depth=[10, 20, 100, None],\n",
+        "        final_estimator__fit_intercept=[True, False]\n",
+        "        )\n",
+        "grid_search = GridSearchCV(clf, param_grid=param_grid)"
+      ],
+      "metadata": {
+        "id": "f4NDA_YVuATX"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "grid_search.fit(X_train,y_train)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1CoFVe3juEF5",
+        "outputId": "69308638-41b5-4401-b456-312a3b1b37af"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "GridSearchCV(estimator=StackingRegressor(cv=5,\n",
+              "                                         estimators=[('kNN',\n",
+              "                                                      Pipeline(steps=[('columntransformer',\n",
+              "                                                                       ColumnTransformer(transformers=[('passthrough',\n",
+              "                                                                                                        'passthrough',\n",
+              "                                                                                                        ['Longitude',\n",
+              "                                                                                                         'Latitude'])])),\n",
+              "                                                                      ('kneighborsregressor',\n",
+              "                                                                       KNeighborsRegressor())])),\n",
+              "                                                     ('random_forest',\n",
+              "                                                      Pipeline(steps=[('columntransformer',\n",
+              "                                                                       ColumnTransformer(transformers=[('passthrough',\n",
+              "                                                                                                        'passthr...\n",
+              "                                                                                                         'AveOccup'])])),\n",
+              "                                                                      ('randomforestregressor',\n",
+              "                                                                       RandomForestRegressor(random_state=42))]))],\n",
+              "                                         final_estimator=LinearRegression()),\n",
+              "             param_grid={'final_estimator__fit_intercept': [True, False],\n",
+              "                         'kNN__kneighborsregressor__n_neighbors': [3, 5, 10,\n",
+              "                                                                   20],\n",
+              "                         'random_forest__randomforestregressor__max_depth': [10,\n",
+              "                                                                             20,\n",
+              "                                                                             100,\n",
+              "                                                                             None],\n",
+              "                         'random_forest__randomforestregressor__min_samples_leaf': [2,\n",
+              "                                                                                    4]})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "param_grid_knn = dict(\n",
+        "        kneighborsregressor__n_neighbors=[3, 5, 10, 20]\n",
+        "        )\n",
+        "\n",
+        "grid_search_knn = GridSearchCV(knn_pipeline, param_grid=param_grid_knn)\n",
+        "grid_search_knn.fit(X_train,y_train)\n",
+        "grid_search_knn.best_estimator_"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ig0QV5LsXSFq",
+        "outputId": "8dce97e0-0044-40b5-855f-7dd58f7556f8"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('columntransformer',\n",
+              "                 ColumnTransformer(transformers=[('passthrough', 'passthrough',\n",
+              "                                                  ['Longitude', 'Latitude'])])),\n",
+              "                ('kneighborsregressor', KNeighborsRegressor())])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "param_grid_rf = dict(\n",
+        "        randomforestregressor__min_samples_leaf=[2, 4],\n",
+        "        randomforestregressor__max_depth=[10, 20, 100, None]\n",
+        ")\n",
+        "\n",
+        "grid_search_rf = GridSearchCV(rf_pipeline, param_grid=param_grid_rf)\n",
+        "grid_search_rf.fit(X_train,y_train)\n",
+        "grid_search_rf.best_estimator_"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XTmIRfR0XbL4",
+        "outputId": "3b35c9d0-cbdc-4a12-d3ee-b67c0b636fe5"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('columntransformer',\n",
+              "                 ColumnTransformer(transformers=[('passthrough', 'passthrough',\n",
+              "                                                  ['MedInc', 'HouseAge',\n",
+              "                                                   'AveRooms', 'AveBedrms',\n",
+              "                                                   'Population',\n",
+              "                                                   'AveOccup'])])),\n",
+              "                ('randomforestregressor',\n",
+              "                 RandomForestRegressor(max_depth=10, min_samples_leaf=4,\n",
+              "                                       random_state=42))])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clf_best = grid_search.best_estimator_\n",
+        "clf_best.fit(X_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OiNBFZniY2JC",
+        "outputId": "f5588707-3dc6-4c96-b84e-676d0af6e4d3"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "StackingRegressor(cv=5,\n",
+              "                  estimators=[('kNN',\n",
+              "                               Pipeline(steps=[('columntransformer',\n",
+              "                                                ColumnTransformer(transformers=[('passthrough',\n",
+              "                                                                                 'passthrough',\n",
+              "                                                                                 ['Longitude',\n",
+              "                                                                                  'Latitude'])])),\n",
+              "                                               ('kneighborsregressor',\n",
+              "                                                KNeighborsRegressor())])),\n",
+              "                              ('random_forest',\n",
+              "                               Pipeline(steps=[('columntransformer',\n",
+              "                                                ColumnTransformer(transformers=[('passthrough',\n",
+              "                                                                                 'passthrough',\n",
+              "                                                                                 ['MedInc',\n",
+              "                                                                                  'HouseAge',\n",
+              "                                                                                  'AveRooms',\n",
+              "                                                                                  'AveBedrms',\n",
+              "                                                                                  'Population',\n",
+              "                                                                                  'AveOccup'])])),\n",
+              "                                               ('randomforestregressor',\n",
+              "                                                RandomForestRegressor(max_depth=10,\n",
+              "                                                                      min_samples_leaf=4,\n",
+              "                                                                      random_state=42))]))],\n",
+              "                  final_estimator=LinearRegression())"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Task 4"
+      ],
+      "metadata": {
+        "id": "PY2gRIFNY9ZM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def mse_test(model, X, y):\n",
+        "    test_pred = model.predict(X)\n",
+        "    mse = mean_squared_error(y, test_pred)\n",
+        "    return mse\n"
+      ],
+      "metadata": {
+        "id": "q29GrDCMY_4V"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"mse_knn_best = \" + str(round(mse_test(grid_search_knn.best_estimator_, X_test, y_test), 4)))\n",
+        "print(\"mse_rf_best = \" + str(round(mse_test(grid_search_rf.best_estimator_, X_test, y_test), 4)))"
+      ],
+      "metadata": {
+        "id": "rJox_HKbZBlB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"mse_knn = \" + str(round(mse_test(knn, X_test[[\"Longitude\", \"Latitude\"]], y_test), 4)))\n",
+        "print(\"mse_rf = \" + str(round(mse_test(rf, X_test.drop([\"Longitude\", \"Latitude\"], axis=1), y_test), 4)))\n",
+        "print(\"mse_clf_best = \" + str(round(mse_test(clf_best, X_test, y_test), 4)))"
+      ],
+      "metadata": {
+        "id": "WNDeNiomZEvq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "MSE_rf > MSE_knn > MSE_clf_best. It indicates that the stacking model has better predictive performance than the other two. By stacking with multiple models, the final prediction becomes more convincing and reduces the overfitting symptom to some extent."
+      ],
+      "metadata": {
+        "id": "BDyw4oLPZOLv"
+      }
+    }
+  ]
+}
\ No newline at end of file