1122_배포용코드.ipynb

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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sb6YU3Afp9-C"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fish = pd.read_csv('https://bit.ly/fish_csv_data')\n",
        "\n",
        "fish.head()\n",
        "fish.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmHzJgLS39oE",
        "outputId": "ea761203-bcc8-46d8-83dd-58ccff93ce36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 159 entries, 0 to 158\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Species   159 non-null    object \n",
            " 1   Weight    159 non-null    float64\n",
            " 2   Length    159 non-null    float64\n",
            " 3   Diagonal  159 non-null    float64\n",
            " 4   Height    159 non-null    float64\n",
            " 5   Width     159 non-null    float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 7.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.unique(fish['Species']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MaFtO3XP4EGq",
        "outputId": "e62cc690-1431-49f6-cd49-f4d58ecc2790"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Roach' 'Whitefish' 'Parkki' 'Perch' 'Pike' 'Smelt']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fish_input = fish[['Weight','Length','Diagonal','Height','Width']].to_numpy()"
      ],
      "metadata": {
        "id": "IRT9Lyn94cO7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(fish_input[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEMf_9fr4tJS",
        "outputId": "0d08b745-bb31-4e13-e949-c6cdc429069c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[242.      25.4     30.      11.52     4.02  ]\n",
            " [290.      26.3     31.2     12.48     4.3056]\n",
            " [340.      26.5     31.1     12.3778   4.6961]\n",
            " [363.      29.      33.5     12.73     4.4555]\n",
            " [430.      29.      34.      12.444    5.134 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fish_target = fish['Species'].to_numpy()"
      ],
      "metadata": {
        "id": "ilfXlUGR5OIq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    fish_input, fish_target, random_state = 42\n",
        ")"
      ],
      "metadata": {
        "id": "cqrRwmlA9-eR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "CMOwpEcI-pVQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ss = StandardScaler()\n",
        "ss.fit(train_input)\n",
        "train_scaled = ss.transform(train_input)\n",
        "test_scaled = ss.transform(test_input)\n",
        "\n"
      ],
      "metadata": {
        "id": "xVv--1DE2d49"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bream_smelt_indexes = (train_target == 'Bream') | (train_target == 'Smelt')\n",
        "\n",
        "train_bream_smelt = train_scaled[bream_smelt_indexes]\n",
        "target_bream_smelt = train_target[bream_smelt_indexes]"
      ],
      "metadata": {
        "id": "WX5IsMHS2kw6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "lr.fit(train_bream_smelt, target_bream_smelt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "chS-6E_J3fOC",
        "outputId": "bb46b1d2-182d-4b3f-f841-9f796316940e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#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>LogisticRegression()</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(lr.predict(train_bream_smelt[:5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PEQ3kOh3hNx",
        "outputId": "770bdeb6-4ad5-45de-a272-daa8043843b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Smelt' 'Bream' 'Bream' 'Bream']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(lr.predict_proba(train_bream_smelt[:5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PW_ocjJG3i5p",
        "outputId": "0a9a6a9f-869e-4a36-b5fd-a7a9e58450fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.99759855 0.00240145]\n",
            " [0.02735183 0.97264817]\n",
            " [0.99486072 0.00513928]\n",
            " [0.98584202 0.01415798]\n",
            " [0.99767269 0.00232731]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(lr.classes_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YcBox8zO3lGi",
        "outputId": "a3effc09-e488-4523-a4ba-b232c1d42d45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Smelt']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression(C=20, max_iter=1000)\n",
        "lr.fit(train_scaled, train_target)\n",
        "\n",
        "print(lr.score(train_scaled, train_target))\n",
        "print(lr.score(test_scaled, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GWoIUFL446Jp",
        "outputId": "2ea5833f-5a6b-4e0a-da38-1a38973aefb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9327731092436975\n",
            "0.925\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(lr.predict(test_scaled[:5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8zwPTMI6bdx",
        "outputId": "27f140b2-1436-4c9c-d6a4-304b09b29e38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Perch' 'Smelt' 'Pike' 'Roach' 'Perch']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "proba = lr.predict_proba(test_scaled[:5])\n",
        "\n",
        "print(np.round(proba, decimals=3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wW4EcGG4-u8C",
        "outputId": "cb6aa557-0eb4-4cc3-81dd-5a61e5397d68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.    0.014 0.841 0.    0.136 0.007 0.003]\n",
            " [0.    0.003 0.044 0.    0.007 0.946 0.   ]\n",
            " [0.    0.    0.034 0.935 0.015 0.016 0.   ]\n",
            " [0.011 0.034 0.306 0.007 0.567 0.    0.076]\n",
            " [0.    0.    0.904 0.002 0.089 0.002 0.001]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decision = lr.decision_function(test_scaled[:5])\n",
        "\n",
        "print(np.round(decision, decimals=2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoMQgWvMHFUM",
        "outputId": "b90a3f87-bc40-4b19-9eae-4845ff214ebd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ -6.5    1.03   5.16  -2.73   3.34   0.33  -0.63]\n",
            " [-10.86   1.93   4.77  -2.4    2.98   7.84  -4.26]\n",
            " [ -4.34  -6.23   3.17   6.49   2.36   2.42  -3.87]\n",
            " [ -0.68   0.45   2.65  -1.19   3.26  -5.75   1.26]\n",
            " [ -6.4   -1.99   5.82  -0.11   3.5   -0.11  -0.71]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.special import softmax\n",
        "\n",
        "proba = softmax(decision, axis=1)\n",
        "print(np.round(proba, decimals=3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmSasuLP-zYX",
        "outputId": "62e0e1b4-156e-4a8e-9b6d-127e87ad84a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.    0.014 0.841 0.    0.136 0.007 0.003]\n",
            " [0.    0.003 0.044 0.    0.007 0.946 0.   ]\n",
            " [0.    0.    0.034 0.935 0.015 0.016 0.   ]\n",
            " [0.011 0.034 0.306 0.007 0.567 0.    0.076]\n",
            " [0.    0.    0.904 0.002 0.089 0.002 0.001]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier"
      ],
      "metadata": {
        "id": "97rXmSQcHHl2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sc = SGDClassifier(loss='log_loss', max_iter=10, random_state=42)\n",
        "sc.fit(train_scaled, train_target)\n",
        "\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCNSHlY3i--4",
        "outputId": "66dc7ad1-945c-4e3e-d3d1-0ab9a2992931"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.773109243697479\n",
            "0.775\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:702: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc.partial_fit(train_scaled, train_target)\n",
        "\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0m1ect9OjBoZ",
        "outputId": "a9ac112e-65ba-4739-988a-b183952201f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8151260504201681\n",
            "0.85\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc = SGDClassifier(loss='log_loss', random_state=42)\n",
        "\n",
        "train_score = []\n",
        "test_score = []\n",
        "\n",
        "classes = np.unique(train_target)"
      ],
      "metadata": {
        "id": "wBl5y1hHjFv5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for _ in range(0, 300):\n",
        "    sc.partial_fit(train_scaled, train_target, classes=classes)\n",
        "\n",
        "    train_score.append(sc.score(train_scaled, train_target))\n",
        "    test_score.append(sc.score(test_scaled, test_target))"
      ],
      "metadata": {
        "id": "HDKaCWpijKOw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_score)\n",
        "plt.plot(test_score)\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "3okbYhQJjMIQ",
        "outputId": "5dad8339-33ca-46bd-9709-d31878bfeff9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc = SGDClassifier(loss='log_loss', max_iter=100, tol=None, random_state=42)\n",
        "sc.fit(train_scaled, train_target)\n",
        "\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzQGHywljULQ",
        "outputId": "e8b05bf9-2e28-40e8-b825-12586595d099"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.957983193277311\n",
            "0.925\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc = SGDClassifier(loss='hinge', max_iter=100, tol=None, random_state=42)\n",
        "sc.fit(train_scaled, train_target)\n",
        "\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfDtmxaxjX3Q",
        "outputId": "46dd5e0b-4569-4131-e659-c1a1186418e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9495798319327731\n",
            "0.925\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine = pd.read_csv('https://bit.ly/wine_csv_data')"
      ],
      "metadata": {
        "id": "Sxt9pByljZag"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wine.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RLDNzCbyA2IT",
        "outputId": "4a6b874a-659d-4f7c-ee82-ba7879781df4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   alcohol  sugar    pH  class\n",
              "0      9.4    1.9  3.51    0.0\n",
              "1      9.8    2.6  3.20    0.0\n",
              "2      9.8    2.3  3.26    0.0\n",
              "3      9.8    1.9  3.16    0.0\n",
              "4      9.4    1.9  3.51    0.0"
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-1d8eb74d-1df4-4d3f-b885-ad10c7c7d8c8');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-66eda93b-3908-4509-9e59-589f4715c66d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdnXZwBgA42P",
        "outputId": "8c88309e-6aab-40d7-9fbd-8b4b2db8ceee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6497 entries, 0 to 6496\n",
            "Data columns (total 4 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   alcohol  6497 non-null   float64\n",
            " 1   sugar    6497 non-null   float64\n",
            " 2   pH       6497 non-null   float64\n",
            " 3   class    6497 non-null   float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 203.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = wine[['alcohol', 'sugar', 'pH']].to_numpy()\n",
        "target = wine['class'].to_numpy()"
      ],
      "metadata": {
        "id": "rYYmLx0OA9f9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    data, target, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "aUdbqIB7BBDJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_input.shape, test_input.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9kpUdg3BC_C",
        "outputId": "f0376b97-c1c4-45d4-d01e-417cc7d553ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5197, 3) (1300, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ss = StandardScaler()\n",
        "ss.fit(train_input)\n",
        "\n",
        "train_scaled = ss.transform(train_input)\n",
        "test_scaled = ss.transform(test_input)"
      ],
      "metadata": {
        "id": "4pIbdC1oBEdh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "ojLEuwkwBJd3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "dt.fit(train_scaled, train_target)\n",
        "\n",
        "print(dt.score(train_scaled, train_target))\n",
        "print(dt.score(test_scaled, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCMo9oIWBQyy",
        "outputId": "b80d3264-002a-4954-c463-35080d9030aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.996921300750433\n",
            "0.8592307692307692\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import plot_tree\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10,7))\n",
        "plot_tree(dt, max_depth=1, filled=True, feature_names=['alcohol', 'sugar', 'pH'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "fSIxjCiBBTTJ",
        "outputId": "ca8e61d1-50df-431b-ad06-e936945e245e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = DecisionTreeClassifier(max_depth=3, random_state=42)\n",
        "dt.fit(train_scaled, train_target)\n",
        "\n",
        "print(dt.score(train_scaled, train_target))\n",
        "print(dt.score(test_scaled, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kwis6v9YBW7T",
        "outputId": "abd45a6e-2558-464a-a98f-b13a224d7787"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8454877814123533\n",
            "0.8415384615384616\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,15))\n",
        "\n",
        "plot_tree(dt, filled = True, feature_names = ['alcohol', 'sugar', 'pH'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LlGdbdHqBpt3",
        "outputId": "bdc176d6-1788-43e1-afa5-d6b1eb515595"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    data, target, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "YVBEDs8UCN6X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sub_input, val_input, sub_target, val_target = train_test_split(\n",
        "    train_input, train_target, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "MOFQnXpOiWsr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(sub_input.shape, val_input.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMXmfwrYiYUo",
        "outputId": "fc4899d4-bf98-4b49-ba3a-c941d4971319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4157, 3) (1040, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "dt.fit(sub_input, sub_target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "3o8-gpARicgc",
        "outputId": "5e939de0-5938-4e47-a3ed-a31ccf0a120d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dt.score(sub_input, sub_target))\n",
        "print(dt.score(val_input, val_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cy3Y6KfjDuX",
        "outputId": "97d0a7cc-b95d-4dc2-dd23-60636b4bd822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9971133028626413\n",
            "0.864423076923077\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "scores = cross_validate(dt, train_input, train_target)\n",
        "print(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7_bRk6L5jFcp",
        "outputId": "64c7e45a-e071-4bd7-c370-d299f99d4bd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'fit_time': array([0.02566195, 0.02106833, 0.04489732, 0.01328802, 0.01173592]), 'score_time': array([0.01615739, 0.00439572, 0.00928116, 0.00179935, 0.00186944]), 'test_score': array([0.86923077, 0.84615385, 0.87680462, 0.84889317, 0.83541867])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.mean(scores['test_score']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6e-8YvajfGV",
        "outputId": "5e195cd6-9256-44be-fd9e-05e4ee6a0b2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.855300214703487\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "scores = cross_validate(dt, train_input, train_target, cv=StratifiedKFold())\n",
        "print(np.mean(scores['test_score']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESP03FZWtll2",
        "outputId": "714bb7a6-c566-48c6-d039-011d4192fd9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.855300214703487\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "splitter = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "scores = cross_validate(dt, train_input, train_target, cv=splitter)\n",
        "print(np.mean(scores['test_score']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4XbYdS0vKHp",
        "outputId": "438303ba-e7f1-4e21-b1f1-69638775a763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8574181117533719\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = {'min_impurity_decrease': [0.0001, 0.0002, 0.0003, 0.0004, 0.0005]}"
      ],
      "metadata": {
        "id": "MgPAtTJxvseG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gs = GridSearchCV(DecisionTreeClassifier(random_state=42), params, n_jobs=-1)\n"
      ],
      "metadata": {
        "id": "yRyYBBo4vsjn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gs.fit(train_input, train_target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "-0hopMk8vsm3",
        "outputId": "a206d334-72e2-44bf-84fd-707e6de68895"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={'min_impurity_decrease': [0.0001, 0.0002, 0.0003,\n",
              "                                                   0.0004, 0.0005]})"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={&#x27;min_impurity_decrease&#x27;: [0.0001, 0.0002, 0.0003,\n",
              "                                                   0.0004, 0.0005]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={&#x27;min_impurity_decrease&#x27;: [0.0001, 0.0002, 0.0003,\n",
              "                                                   0.0004, 0.0005]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = gs.best_estimator_\n",
        "print(dt.score(train_input, train_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uckzBI3QvspJ",
        "outputId": "0f1675b7-e116-4403-d207-3f2798426aee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9615162593804117\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(gs.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xms1DaNRJ48r",
        "outputId": "491c3c4a-fa87-4708-861a-23866285e570"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'min_impurity_decrease': 0.0001}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params = {'min_impurity_decrease': np.arange(0.0001, 0.001, 0.0001),\n",
        "          'max_depth': range(20, 51),\n",
        "          'min_samples_split': range(2,26),\n",
        "          'min_samples_leaf': range(1,26),\n",
        "          }"
      ],
      "metadata": {
        "id": "4shhXiv4LI90"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "rs = RandomizedSearchCV(DecisionTreeClassifier(random_state=42), params,\n",
        "                        n_iter=100, n_jobs=-1, random_state=42)\n",
        "rs.fit(train_input, train_target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "42wgvGnWMOl8",
        "outputId": "65a21b3d-1750-4b1f-ccc4-82b8565852fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=42),\n",
              "                   n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={'max_depth': range(20, 51),\n",
              "                                        'min_impurity_decrease': array([0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008,\n",
              "       0.0009]),\n",
              "                                        'min_samples_leaf': range(1, 26),\n",
              "                                        'min_samples_split': range(2, 26)},\n",
              "                   random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=42),\n",
              "                   n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: range(20, 51),\n",
              "                                        &#x27;min_impurity_decrease&#x27;: array([0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008,\n",
              "       0.0009]),\n",
              "                                        &#x27;min_samples_leaf&#x27;: range(1, 26),\n",
              "                                        &#x27;min_samples_split&#x27;: range(2, 26)},\n",
              "                   random_state=42)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=42),\n",
              "                   n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: range(20, 51),\n",
              "                                        &#x27;min_impurity_decrease&#x27;: array([0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008,\n",
              "       0.0009]),\n",
              "                                        &#x27;min_samples_leaf&#x27;: range(1, 26),\n",
              "                                        &#x27;min_samples_split&#x27;: range(2, 26)},\n",
              "                   random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(rs.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHN7NMVpMbv8",
        "outputId": "eceb8cae-8d6c-4058-b70a-44ba153ddc80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'min_samples_split': 18, 'min_samples_leaf': 4, 'min_impurity_decrease': 0.0004, 'max_depth': 39}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.max(rs.cv_results_['mean_test_score']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgs3dOrMMfD8",
        "outputId": "0a9508a8-b6b9-4f7b-db2e-2ef051b83ea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8685792552010069\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = rs.best_estimator_\n",
        "\n",
        "print(dt.score(test_input, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oGWooldMh38",
        "outputId": "bfe8943d-133b-44b9-9dad-02d0661c2401"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8592307692307692\n"
          ]
        }
      ]
    }
  ]
}