Week5Lab.ipynb

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        "id": "wijlEVIlAVOx"
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      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt # for data visualization purposes\n",
        "import seaborn as sns # for data visualization\n",
        "%matplotlib inline\n"
      ]
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      "source": [
        "df = pd.read_csv('breast-cancer-wisconsin.data.txt', header=None)"
      ],
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        "id": "BDlUlvOdAiy0"
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      "execution_count": 27,
      "outputs": []
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        "col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']"
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      "metadata": {
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      "execution_count": 28,
      "outputs": []
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      "source": [
        "df.columns = col_names"
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      "execution_count": 29,
      "outputs": []
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        "df.head()"
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              "        Id  Clump_thickness  Uniformity_Cell_Size  Uniformity_Cell_Shape  \\\n",
              "0  1000025                5                     1                      1   \n",
              "1  1002945                5                     4                      4   \n",
              "2  1015425                3                     1                      1   \n",
              "3  1016277                6                     8                      8   \n",
              "4  1017023                4                     1                      1   \n",
              "\n",
              "   Marginal_Adhesion  Single_Epithelial_Cell_Size Bare_Nuclei  \\\n",
              "0                  1                            2           1   \n",
              "1                  5                            7          10   \n",
              "2                  1                            2           2   \n",
              "3                  1                            3           4   \n",
              "4                  3                            2           1   \n",
              "\n",
              "   Bland_Chromatin  Normal_Nucleoli  Mitoses  Class  \n",
              "0                3                1        1      2  \n",
              "1                3                2        1      2  \n",
              "2                3                1        1      2  \n",
              "3                3                7        1      2  \n",
              "4                3                1        1      2  "
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        "df.drop('Id', axis=1, inplace=True)"
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            "text/plain": [
              "(699, 10)"
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          "execution_count": 32
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              "Clump_thickness                 int64\n",
              "Uniformity_Cell_Size            int64\n",
              "Uniformity_Cell_Shape           int64\n",
              "Marginal_Adhesion               int64\n",
              "Single_Epithelial_Cell_Size     int64\n",
              "Bare_Nuclei                    object\n",
              "Bland_Chromatin                 int64\n",
              "Normal_Nucleoli                 int64\n",
              "Mitoses                         int64\n",
              "Class                           int64\n",
              "dtype: object"
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              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Bare_Nuclei'] = pd.to_numeric(df['Bare_Nuclei'], errors='coerce')"
      ],
      "metadata": {
        "id": "8RcFq6ATA-of"
      },
      "execution_count": 34,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# check the NaN and remove the entire row\n",
        "df.isnull().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
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        "id": "Uj9NtZOABBsG",
        "outputId": "6f5f60c2-19b7-4efb-fb94-e45714e62dd1"
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      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "Clump_thickness                 0\n",
              "Uniformity_Cell_Size            0\n",
              "Uniformity_Cell_Shape           0\n",
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              "Single_Epithelial_Cell_Size     0\n",
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      "source": [
        "df1 = df.dropna()"
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      "execution_count": 36,
      "outputs": []
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        "# view frequency distribution of values in `Class` variable\n",
        "df1['Class'].value_counts()\n"
      ],
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          "base_uri": "https://localhost:8080/",
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      "execution_count": 37,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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      "source": [
        "X = df1.drop(['Class'], axis=1)\n",
        "y = df1['Class']\n"
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      "execution_count": 38,
      "outputs": []
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        "df1['Class'].value_counts()"
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        "id": "NRC59ye9DEVF",
        "outputId": "48877cc8-6f3c-4289-c2bb-73ec640440cb"
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      "execution_count": 40,
      "outputs": [
        {
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          "data": {
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      "execution_count": 41,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Clump_thickness  Uniformity_Cell_Size  Uniformity_Cell_Shape  \\\n",
              "0                5                     1                      1   \n",
              "1                5                     4                      4   \n",
              "2                3                     1                      1   \n",
              "3                6                     8                      8   \n",
              "4                4                     1                      1   \n",
              "\n",
              "   Marginal_Adhesion  Single_Epithelial_Cell_Size  Bare_Nuclei  \\\n",
              "0                  1                            2          1.0   \n",
              "1                  5                            7         10.0   \n",
              "2                  1                            2          2.0   \n",
              "3                  1                            3          4.0   \n",
              "4                  3                            2          1.0   \n",
              "\n",
              "   Bland_Chromatin  Normal_Nucleoli  Mitoses  \n",
              "0                3                1        1  \n",
              "1                3                2        1  \n",
              "2                3                1        1  \n",
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            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the train_test_split and split the train and test dataset.\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=10)"
      ],
      "metadata": {
        "id": "ZDt6jVmMBijn"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check the shape of X_train and X_test\n",
        "X_train.shape, X_test.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rhoYvQjrCEwO",
        "outputId": "ded319aa-e586-490b-ade9-452d9ae05aa8"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((478, 9), (205, 9))"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import KNeighbors ClaSSifier from sklearn\n",
        "from sklearn.neighbors import KNeighborsClassifier\n"
      ],
      "metadata": {
        "id": "gwwSkSHmCgUn"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# initiate the model\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n"
      ],
      "metadata": {
        "id": "KOUv41s_DoRp"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the model to the training set\n",
        "knn.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 95
        },
        "id": "V6PwDi95Dsbw",
        "outputId": "8137017f-19d6-4c21-8cc9-46a3ed919d11"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print the scores on training and test set\n",
        "print('Training set score: {:.4f}'.format(knn.score(X_train, y_train)))\n",
        "print('Test set score: {:.4f}'.format(knn.score(X_test, y_test)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5l5oX6qDyq0",
        "outputId": "50e08084-d1ca-42e1-a4cb-beec6a87c077"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set score: 0.9833\n",
            "Test set score: 0.9805\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "y_pred = knn.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20B9QzKDD40Z",
        "outputId": "7670201d-c438-478b-fc55-37529902e804"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           2       0.98      0.98      0.98       131\n",
            "           4       0.97      0.97      0.97        74\n",
            "\n",
            "    accuracy                           0.98       205\n",
            "   macro avg       0.98      0.98      0.98       205\n",
            "weighted avg       0.98      0.98      0.98       205\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Write a code to find the accuracy with k value of [3,5,7,9,11,15,17,21,25] and plot the accuracy with k values. (x-axis is k value and y-axis is accuracy)."
      ],
      "metadata": {
        "id": "8EMZNhVjEN7w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ls = [3,5,7,9,11,15,17,21,25]\n",
        "res = []\n",
        "for k in ls:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train, y_train)\n",
        "    acc = knn.score(X_test,y_test)\n",
        "    print('Test set score: {:.4f}'.format(acc))\n",
        "    res.append(acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6Rig27tEL7I",
        "outputId": "bb608635-269b-488c-fbdf-b827b306a3d5"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set score: 0.9805\n",
            "Test set score: 0.9805\n",
            "Test set score: 0.9805\n",
            "Test set score: 0.9756\n",
            "Test set score: 0.9707\n",
            "Test set score: 0.9756\n",
            "Test set score: 0.9756\n",
            "Test set score: 0.9707\n",
            "Test set score: 0.9707\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ls, res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "iiNhCbzlE_wR",
        "outputId": "d15c29ab-a995-422e-cc22-0d27ccccfd87"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b77b695dbd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TASK 2\n"
      ],
      "metadata": {
        "id": "WUCkDTW7E04e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt # data visualization\n",
        "import seaborn as sns # statistical data visualization\n",
        "%matplotlib inline\n"
      ],
      "metadata": {
        "id": "6XE-sXgKE3yE"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf_gini = DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=3)\n",
        "clf_gini.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 95
        },
        "id": "eXQ2HFxHEOs3",
        "outputId": "b9985888-7d6c-4c5f-ad20-13f4471045f4"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=3, random_state=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, random_state=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print('Training set score: {:.4f}'.format(clf_gini.score(X_train, y_train)))\n",
        "print('Test set score: {:.4f}'.format(clf_gini.score(X_test, y_test)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpN5YUETFbH6",
        "outputId": "5c5f76f1-b363-4a31-a6f7-e2fd1e49451f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set score: 0.9686\n",
            "Test set score: 0.9659\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import graphviz\n",
        "from sklearn import tree\n",
        "dot_data = tree.export_graphviz(clf_gini, out_file=None, feature_names=X_train.columns, class_names=True, filled=True, rounded=True, special_characters=True)\n",
        "graph = graphviz.Source(dot_data)\n",
        "graph\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "sUirFF9EFgok",
        "outputId": "500a5606-5eb8-4a86-a85a-8b5064eea7b2"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"952pt\" height=\"414pt\"\n viewBox=\"0.00 0.00 952.00 414.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 410)\">\n<title>Tree</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-410 948,-410 948,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#f3c3a1\" stroke=\"black\" d=\"M544,-406C544,-406 392,-406 392,-406 386,-406 380,-400 380,-394 380,-394 380,-340 380,-340 380,-334 386,-328 392,-328 392,-328 544,-328 544,-328 550,-328 556,-334 556,-340 556,-340 556,-394 556,-394 556,-400 550,-406 544,-406\"/>\n<text text-anchor=\"start\" x=\"388\" y=\"-391.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Uniformity_Cell_Size ≤ 3.5</text>\n<text text-anchor=\"start\" x=\"432.5\" y=\"-377.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.452</text>\n<text text-anchor=\"start\" x=\"423\" y=\"-363.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 478</text>\n<text text-anchor=\"start\" x=\"413.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [313, 165]</text>\n<text text-anchor=\"start\" x=\"437.5\" y=\"-335.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = y</text>\n<text text-anchor=\"start\" x=\"491.5\" y=\"-335.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#e78d4b\" stroke=\"black\" d=\"M432,-292C432,-292 332,-292 332,-292 326,-292 320,-286 320,-280 320,-280 320,-226 320,-226 320,-220 326,-214 332,-214 332,-214 432,-214 432,-214 438,-214 444,-220 444,-226 444,-226 444,-280 444,-280 444,-286 438,-292 432,-292\"/>\n<text text-anchor=\"start\" x=\"328\" y=\"-277.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Bare_Nuclei ≤ 2.5</text>\n<text text-anchor=\"start\" x=\"346.5\" y=\"-263.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.154</text>\n<text text-anchor=\"start\" x=\"337\" y=\"-249.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 333</text>\n<text text-anchor=\"start\" x=\"331.5\" y=\"-235.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [305, 28]</text>\n<text text-anchor=\"start\" x=\"351.5\" y=\"-221.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = y</text>\n<text text-anchor=\"start\" x=\"405.5\" y=\"-221.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"black\" d=\"M438.64,-327.77C431.79,-318.85 424.43,-309.26 417.35,-300.04\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"420.09,-297.86 411.22,-292.06 414.54,-302.12 420.09,-297.86\"/>\n<text text-anchor=\"middle\" x=\"407.97\" y=\"-313.15\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n</g>\n<!-- 8 -->\n<g id=\"node9\" class=\"node\">\n<title>8</title>\n<path fill=\"#45a3e7\" stroke=\"black\" d=\"M626.5,-292C626.5,-292 495.5,-292 495.5,-292 489.5,-292 483.5,-286 483.5,-280 483.5,-280 483.5,-226 483.5,-226 483.5,-220 489.5,-214 495.5,-214 495.5,-214 626.5,-214 626.5,-214 632.5,-214 638.5,-220 638.5,-226 638.5,-226 638.5,-280 638.5,-280 638.5,-286 632.5,-292 626.5,-292\"/>\n<text text-anchor=\"start\" x=\"491.5\" y=\"-277.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Bland_Chromatin ≤ 4.5</text>\n<text text-anchor=\"start\" x=\"525.5\" y=\"-263.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.104</text>\n<text text-anchor=\"start\" x=\"516\" y=\"-249.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 145</text>\n<text text-anchor=\"start\" x=\"514\" y=\"-235.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [8, 137]</text>\n<text text-anchor=\"start\" x=\"530.5\" y=\"-221.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = y</text>\n<text text-anchor=\"start\" x=\"584.5\" y=\"-221.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n</g>\n<!-- 0&#45;&gt;8 -->\n<g id=\"edge8\" class=\"edge\">\n<title>0&#45;&gt;8</title>\n<path fill=\"none\" stroke=\"black\" d=\"M499.75,-327.77C507.23,-318.76 515.28,-309.06 523,-299.76\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"525.7,-301.99 529.4,-292.06 520.32,-297.52 525.7,-301.99\"/>\n<text text-anchor=\"middle\" x=\"531.71\" y=\"-313.25\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"#e5823a\" stroke=\"black\" d=\"M239,-178C239,-178 113,-178 113,-178 107,-178 101,-172 101,-166 101,-166 101,-112 101,-112 101,-106 107,-100 113,-100 113,-100 239,-100 239,-100 245,-100 251,-106 251,-112 251,-112 251,-166 251,-166 251,-172 245,-178 239,-178\"/>\n<text text-anchor=\"start\" x=\"109\" y=\"-163.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Normal_Nucleoli ≤ 8.5</text>\n<text text-anchor=\"start\" x=\"140.5\" y=\"-149.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.014</text>\n<text text-anchor=\"start\" x=\"131\" y=\"-135.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 289</text>\n<text text-anchor=\"start\" x=\"129\" y=\"-121.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [287, 2]</text>\n<text text-anchor=\"start\" x=\"145.5\" y=\"-107.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = y</text>\n<text text-anchor=\"start\" x=\"199.5\" y=\"-107.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n</g>\n<!-- 1&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>1&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M319.91,-218.24C299.51,-207.15 276.54,-194.66 255.05,-182.98\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"256.71,-179.9 246.26,-178.2 253.37,-186.05 256.71,-179.9\"/>\n</g>\n<!-- 5 -->\n<g id=\"node6\" class=\"node\">\n<title>5</title>\n<path fill=\"#c2e1f7\" stroke=\"black\" d=\"M447,-178C447,-178 317,-178 317,-178 311,-178 305,-172 305,-166 305,-166 305,-112 305,-112 305,-106 311,-100 317,-100 317,-100 447,-100 447,-100 453,-100 459,-106 459,-112 459,-112 459,-166 459,-166 459,-172 453,-178 447,-178\"/>\n<text text-anchor=\"start\" x=\"313\" y=\"-163.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Clump_thickness ≤ 3.5</text>\n<text text-anchor=\"start\" x=\"346.5\" y=\"-149.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.483</text>\n<text text-anchor=\"start\" x=\"341\" y=\"-135.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 44</text>\n<text text-anchor=\"start\" x=\"335\" y=\"-121.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [18, 26]</text>\n<text text-anchor=\"start\" x=\"351.5\" y=\"-107.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = y</text>\n<text text-anchor=\"start\" x=\"405.5\" y=\"-107.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n</g>\n<!-- 1&#45;&gt;5 -->\n<g id=\"edge5\" class=\"edge\">\n<title>1&#45;&gt;5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M382,-213.77C382,-205.57 382,-196.8 382,-188.28\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"385.5,-188.06 382,-178.06 378.5,-188.06 385.5,-188.06\"/>\n</g>\n<!-- 3 -->\n<g 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class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M131.97,-99.8C121.1,-90.36 109.46,-80.25 98.6,-70.84\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"100.71,-68.03 90.86,-64.12 96.12,-73.31 100.71,-68.03\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M211.5,-64C211.5,-64 140.5,-64 140.5,-64 134.5,-64 128.5,-58 128.5,-52 128.5,-52 128.5,-12 128.5,-12 128.5,-6 134.5,0 140.5,0 140.5,0 211.5,0 211.5,0 217.5,0 223.5,-6 223.5,-12 223.5,-12 223.5,-52 223.5,-52 223.5,-58 217.5,-64 211.5,-64\"/>\n<text text-anchor=\"start\" x=\"148\" y=\"-49.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"138.5\" y=\"-35.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"start\" x=\"136.5\" y=\"-21.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n<text text-anchor=\"start\" x=\"145.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = y</text>\n<text text-anchor=\"start\" x=\"199.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">1</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M176,-99.8C176,-91.46 176,-82.6 176,-74.15\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"179.5,-74.12 176,-64.12 172.5,-74.12 179.5,-74.12\"/>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<path fill=\"#e99355\" stroke=\"black\" d=\"M332,-64C332,-64 254,-64 254,-64 248,-64 242,-58 242,-52 242,-52 242,-12 242,-12 242,-6 248,0 254,0 254,0 332,0 332,0 338,0 344,-6 344,-12 344,-12 344,-52 344,-52 344,-58 338,-64 332,-64\"/>\n<text text-anchor=\"start\" x=\"257.5\" y=\"-49.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.219</text>\n<text text-anchor=\"start\" x=\"252\" y=\"-35.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n<text text-anchor=\"start\" x=\"250\" y=\"-21.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [14, 2]</text>\n<text text-anchor=\"start\" x=\"262.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = y</text>\n<text text-anchor=\"start\" x=\"316.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\">0</text>\n</g>\n<!-- 5&#45;&gt;6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>5&#45;&gt;6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M349.62,-99.8C341.93,-90.73 333.72,-81.04 326,-71.94\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"328.51,-69.48 319.38,-64.12 323.17,-74.01 328.51,-69.48\"/>\n</g>\n<!-- 7 -->\n<g id=\"node8\" class=\"node\">\n<title>7</title>\n<path fill=\"#5aade9\" stroke=\"black\" d=\"M452,-64C452,-64 374,-64 374,-64 368,-64 362,-58 362,-52 362,-52 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stroke=\"black\" points=\"404.34,-74.7 403.81,-64.12 397.62,-72.72 404.34,-74.7\"/>\n</g>\n<!-- 9 -->\n<g id=\"node10\" class=\"node\">\n<title>9</title>\n<path fill=\"#5dafea\" stroke=\"black\" d=\"M626,-178C626,-178 496,-178 496,-178 490,-178 484,-172 484,-166 484,-166 484,-112 484,-112 484,-106 490,-100 496,-100 496,-100 626,-100 626,-100 632,-100 638,-106 638,-112 638,-112 638,-166 638,-166 638,-172 632,-178 626,-178\"/>\n<text text-anchor=\"start\" x=\"492\" y=\"-163.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Clump_thickness ≤ 6.5</text>\n<text text-anchor=\"start\" x=\"525.5\" y=\"-149.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.263</text>\n<text text-anchor=\"start\" x=\"520\" y=\"-135.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 45</text>\n<text text-anchor=\"start\" x=\"518\" y=\"-121.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [7, 38]</text>\n<text text-anchor=\"start\" x=\"530.5\" 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            "text/plain": [
              "<graphviz.sources.Source at 0x7b77b69b3370>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_gini = clf_gini.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_gini))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIoGmN0_Fjmj",
        "outputId": "ea081441-0890-4034-840f-baf34af538d1"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           2       0.98      0.96      0.97       131\n",
            "           4       0.94      0.97      0.95        74\n",
            "\n",
            "    accuracy                           0.97       205\n",
            "   macro avg       0.96      0.97      0.96       205\n",
            "weighted avg       0.97      0.97      0.97       205\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls = [3,4,5,6,7,8,9]\n",
        "res = []\n",
        "for k in ls:\n",
        "    clf_gini = DecisionTreeClassifier(criterion='gini', max_depth=k, random_state=5)\n",
        "    clf_gini.fit(X_train, y_train)\n",
        "    acc = clf_gini.score(X_test,y_test)\n",
        "    print('Test set score: {:.4f}'.format(acc))\n",
        "    res.append(acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jGiPJQS0FrTy",
        "outputId": "9b2c574f-1044-47b5-b79b-a596da4057d6"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set score: 0.9659\n",
            "Test set score: 0.9561\n",
            "Test set score: 0.9610\n",
            "Test set score: 0.9707\n",
            "Test set score: 0.9659\n",
            "Test set score: 0.9561\n",
            "Test set score: 0.9561\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ls, res)\n",
        "plt.xlabel('max_depth')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "UG5UhMYCGCyD",
        "outputId": "b57f2af2-dcae-4f0f-bbee-ca9f906a4fe3"
      },
      "execution_count": 63,
      "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": [
        "#use entropy\n",
        "ls = [3,4,5,6,7,8,9]\n",
        "res = []\n",
        "for k in ls:\n",
        "    clf_gini = DecisionTreeClassifier(criterion='entropy', max_depth=k, random_state=5)\n",
        "    clf_gini.fit(X_train, y_train)\n",
        "    acc = clf_gini.score(X_test,y_test)\n",
        "    print('Test set score: {:.4f}'.format(acc))\n",
        "    res.append(acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruL5MrRDGl41",
        "outputId": "15ec7d63-c92c-4106-e69f-4add35077872"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set score: 0.9659\n",
            "Test set score: 0.9756\n",
            "Test set score: 0.9707\n",
            "Test set score: 0.9707\n",
            "Test set score: 0.9659\n",
            "Test set score: 0.9659\n",
            "Test set score: 0.9659\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ls, res)\n",
        "plt.xlabel('max_depth')\n",
        "plt.ylabel('accuracy')\n",
        "plt.title(\"Entropy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "0BChcqR5HZ89",
        "outputId": "3e0ce90b-9f27-47e5-d3c0-884c2101d966"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task 3"
      ],
      "metadata": {
        "id": "ygebG1fTHvxB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import Random Forest classifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n"
      ],
      "metadata": {
        "id": "PyGohqSuHw7k"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a Random Forest Classifier\n",
        "rfc = RandomForestClassifier(random_state=42)"
      ],
      "metadata": {
        "id": "U1o9I6IYHxoE"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fit the model\n",
        "rfc.fit(X_train, y_train)\n",
        "\n",
        "y_pred = rfc.predict(X_test)"
      ],
      "metadata": {
        "id": "VA5ivXgmH4qu"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nepE0bgjITFV",
        "outputId": "ed589789-9e2a-470d-9453-37a4556f9575"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           2       0.98      0.98      0.98       131\n",
            "           4       0.97      0.97      0.97        74\n",
            "\n",
            "    accuracy                           0.98       205\n",
            "   macro avg       0.98      0.98      0.98       205\n",
            "weighted avg       0.98      0.98      0.98       205\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view the feature scores\n",
        "feature_scores = pd.Series(rfc.feature_importances_, index=X_train.columns).sort_values(ascending=False)\n",
        "print(feature_scores)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHFK5uLDIipf",
        "outputId": "3c72f741-5aba-48e9-abb6-57026cb621cb"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Uniformity_Cell_Size           0.262315\n",
            "Uniformity_Cell_Shape          0.224361\n",
            "Bare_Nuclei                    0.191424\n",
            "Single_Epithelial_Cell_Size    0.088087\n",
            "Normal_Nucleoli                0.087323\n",
            "Bland_Chromatin                0.064529\n",
            "Clump_thickness                0.050007\n",
            "Marginal_Adhesion              0.022183\n",
            "Mitoses                        0.009771\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a seaborn bar plot\n",
        "f, ax = plt.subplots(figsize=(30, 24))\n",
        "ax = sns.barplot(x=feature_scores, y=feature_scores.index)\n",
        "ax.set_title(\"Visualize feature scores of the features\")\n",
        "ax.set_yticklabels(feature_scores.index)\n",
        "ax.set_xlabel(\"Feature importance score\")\n",
        "ax.set_ylabel(\"Features\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "ND3OSV3PIkE9",
        "outputId": "68fe21e3-02fe-4298-d703-d0dc611dba0c"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-73-e34ca99d8875>:5: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_yticklabels(feature_scores.index)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x2400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5uHQMMi3Imk8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}