230630_의사결정나무.ipynb

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        "의사결정나무\n",
        "노드를 만들어서 분리 시키는 것\n",
        "그리고 불순도를 측정하는 방법\n",
        "\n",
        "\n",
        "* cart알고리즘: gini불순도 측정(불순도=impurity)\n",
        "* gini는 불순도와 엔트로피를 계산\n",
        "information gain\n",
        "* 어떤 알고리즘이든 돌리는 방식과 규제하는 파라미터를 이해하면 좋다\n",
        "* 의사결정나무의 규제는 max_depth, min_sample_split등이 있습니다.\n",
        "* 변수 중요도"
      ],
      "metadata": {
        "id": "G1mSZHNJqxaf"
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    {
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      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
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        "id": "YOabuCA_sS7b"
      },
      "execution_count": 130,
      "outputs": []
    },
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      "source": [
        "penguins = pd.read_csv('penguins.csv')"
      ],
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        "id": "73leiSk7tNHw"
      },
      "execution_count": 131,
      "outputs": []
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        "df = penguins.copy()\n",
        "df.head()"
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              "  species     island  culmen_length_mm  culmen_depth_mm  flipper_length_mm  \\\n",
              "0  Adelie  Torgersen              39.1             18.7              181.0   \n",
              "1  Adelie  Torgersen              39.5             17.4              186.0   \n",
              "2  Adelie  Torgersen              40.3             18.0              195.0   \n",
              "3  Adelie  Torgersen               NaN              NaN                NaN   \n",
              "4  Adelie  Torgersen              36.7             19.3              193.0   \n",
              "\n",
              "   body_mass_g     sex  \n",
              "0       3750.0    MALE  \n",
              "1       3800.0  FEMALE  \n",
              "2       3250.0  FEMALE  \n",
              "3          NaN     NaN  \n",
              "4       3450.0  FEMALE  "
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <th>4</th>\n",
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              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
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            "text/plain": [
              "       culmen_length_mm  culmen_depth_mm  flipper_length_mm  body_mass_g\n",
              "count        342.000000       342.000000         342.000000   342.000000\n",
              "mean          43.921930        17.151170         200.915205  4201.754386\n",
              "std            5.459584         1.974793          14.061714   801.954536\n",
              "min           32.100000        13.100000         172.000000  2700.000000\n",
              "25%           39.225000        15.600000         190.000000  3550.000000\n",
              "50%           44.450000        17.300000         197.000000  4050.000000\n",
              "75%           48.500000        18.700000         213.000000  4750.000000\n",
              "max           59.600000        21.500000         231.000000  6300.000000"
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              "      <th>count</th>\n",
              "      <td>342.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>43.921930</td>\n",
              "      <td>17.151170</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
              "      <td>32.100000</td>\n",
              "      <td>13.100000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>2700.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>39.225000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>44.450000</td>\n",
              "      <td>17.300000</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>4050.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>48.500000</td>\n",
              "      <td>18.700000</td>\n",
              "      <td>213.000000</td>\n",
              "      <td>4750.000000</td>\n",
              "    </tr>\n",
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              "      <th>max</th>\n",
              "      <td>59.600000</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>231.000000</td>\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 344 entries, 0 to 343\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   species            344 non-null    object \n",
            " 1   island             344 non-null    object \n",
            " 2   culmen_length_mm   342 non-null    float64\n",
            " 3   culmen_depth_mm    342 non-null    float64\n",
            " 4   flipper_length_mm  342 non-null    float64\n",
            " 5   body_mass_g        342 non-null    float64\n",
            " 6   sex                334 non-null    object \n",
            "dtypes: float64(4), object(3)\n",
            "memory usage: 18.9+ KB\n"
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        },
        "id": "pzjR9mywuEOu",
        "outputId": "f0f925f3-230b-4267-8590-e94582a8b223"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(344, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5g3m9YSYuGLC",
        "outputId": "56bee613-fa22-4884-f45a-7df5cea966b0"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "species               0\n",
              "island                0\n",
              "culmen_length_mm      2\n",
              "culmen_depth_mm       2\n",
              "flipper_length_mm     2\n",
              "body_mass_g           2\n",
              "sex                  10\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['species'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1G6wXPLuJ8V",
        "outputId": "e15be034-9f82-4d97-d2fb-9fe50e9101eb"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adelie       152\n",
              "Gentoo       124\n",
              "Chinstrap     68\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['island'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWGIfP3JvR21",
        "outputId": "326f52b3-3c71-477f-9914-4065bfcac352"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Biscoe       168\n",
              "Dream        124\n",
              "Torgersen     52\n",
              "Name: island, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['sex'] = df['sex'].replace('.', np.nan)\n",
        "df['sex']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFRvVmvLvYlZ",
        "outputId": "3e637065-1e1f-4e39-d553-74189aebe718"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        MALE\n",
              "1      FEMALE\n",
              "2      FEMALE\n",
              "3         NaN\n",
              "4      FEMALE\n",
              "        ...  \n",
              "339       NaN\n",
              "340    FEMALE\n",
              "341      MALE\n",
              "342    FEMALE\n",
              "343      MALE\n",
              "Name: sex, Length: 344, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.copy()"
      ],
      "metadata": {
        "id": "LsbPdv3uv2g3"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqU51IRdxivp",
        "outputId": "71aeb011-b778-4315-bf12-f39869dc8bf0"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['species', 'island', 'culmen_length_mm', 'culmen_depth_mm',\n",
              "       'flipper_length_mm', 'body_mass_g', 'sex'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = ['culmen_length_mm', 'culmen_depth_mm','flipper_length_mm', 'body_mass_g']"
      ],
      "metadata": {
        "id": "5F74U8JHxaR0"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cols:\n",
        "  df1[col].fillna(df[col].mean(), inplace=True)\n",
        "df1.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rm4H7mPS0XTK",
        "outputId": "10082a3d-e5e3-4ba1-d843-86bfe3eb1d88"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "species               0\n",
              "island                0\n",
              "culmen_length_mm      0\n",
              "culmen_depth_mm       0\n",
              "flipper_length_mm     0\n",
              "body_mass_g           0\n",
              "sex                  11\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### sklearn simple imputation으로 결측치 처리하기"
      ],
      "metadata": {
        "id": "Q8fXvO7K0pPE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "df2 = df.copy()\n",
        "si = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "si.fit(df2[cols])\n",
        "df2[cols] = si.transform(df2[cols])\n",
        "df2.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jc4YB__81ED9",
        "outputId": "21b576ac-1571-46b2-b527-00a1f7195121"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "species               0\n",
              "island                0\n",
              "culmen_length_mm      0\n",
              "culmen_depth_mm       0\n",
              "flipper_length_mm     0\n",
              "body_mass_g           0\n",
              "sex                  11\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = df.copy()\n",
        "df3 = df3.dropna()\n",
        "df3.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shFrbks21WEd",
        "outputId": "46575e0b-6e21-4c85-b62a-a10206c7fa83"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 333 entries, 0 to 343\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   species            333 non-null    object \n",
            " 1   island             333 non-null    object \n",
            " 2   culmen_length_mm   333 non-null    float64\n",
            " 3   culmen_depth_mm    333 non-null    float64\n",
            " 4   flipper_length_mm  333 non-null    float64\n",
            " 5   body_mass_g        333 non-null    float64\n",
            " 6   sex                333 non-null    object \n",
            "dtypes: float64(4), object(3)\n",
            "memory usage: 20.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df2.dropna()\n",
        "y = df2.iloc[:,0]\n",
        "X = df2.iloc[:,1:]"
      ],
      "metadata": {
        "id": "lB1-iJR92ohB"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "1BhBnazM3msw"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = ['island', 'sex']\n",
        "num_cols = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm','body_mass_g']"
      ],
      "metadata": {
        "id": "m0P-WEfE376u"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "one_hot endcoding\n",
        "범주형 변수중에 순서가 없는 nominal"
      ],
      "metadata": {
        "id": "kfz_aVuK5Hl6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "train데이터 전처리"
      ],
      "metadata": {
        "id": "-pUjLkGVDAlv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "oh = OneHotEncoder(sparse_output=False)\n",
        "X_train_cat = oh.fit_transform(X_train[['island', 'sex']])"
      ],
      "metadata": {
        "id": "PpxkGSYD5YP7"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "ss = StandardScaler()\n",
        "X_train_num = ss.fit_transform(X_train[num_cols])\n",
        "X_train_num"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNDzXsS-7wsl",
        "outputId": "26ed1adc-da83-471e-d98d-8365162ac395"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.59372706, -1.75093934,  0.9359428 ,  0.53973681],\n",
              "       [-1.26104283,  0.32310698, -0.719956  , -1.07236416],\n",
              "       [ 1.09309891, -0.63803644,  1.65589881,  1.40779118],\n",
              "       ...,\n",
              "       [-0.81616565,  0.27252048, -0.5759648 , -1.25837581],\n",
              "       [ 0.2589542 , -1.0933149 ,  1.36791641,  0.97376399],\n",
              "       [-1.11275044, -0.08158499, -1.43991201, -1.28937775]])"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_con = np.concatenate((X_train_cat, X_train_num), axis=1)"
      ],
      "metadata": {
        "id": "yQUXDhny7Q4X"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "y_train = le.fit_transform(y_train)"
      ],
      "metadata": {
        "id": "vsIGEddo72fS"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "모델만들기"
      ],
      "metadata": {
        "id": "HA-D8rMQDE37"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_train_con, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "yPgMx-Gv8RbT",
        "outputId": "30a9ca9b-f8cf-4680-900f-cd1d126c9834"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "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>DecisionTreeClassifier()</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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import plot_tree\n",
        "plt.figure(figsize=(13,8))\n",
        "plot_tree(clf,filled=True, fontsize=8)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "AhNdKMZd8dz-",
        "outputId": "c973aef7-b56d-4fb5-e7fa-6495633f34c2"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "test데이터 onehotencoding, standardscaler,LabelEncoder"
      ],
      "metadata": {
        "id": "ywLV9vdbCti3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_cat = oh.fit_transform(X_test[['island', 'sex']])"
      ],
      "metadata": {
        "id": "qxik1hj98-XU"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_num = ss.fit_transform(X_test[num_cols])"
      ],
      "metadata": {
        "id": "HItODHX3-JkA"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_con = np.concatenate((X_test_cat, X_test_num), axis=1)"
      ],
      "metadata": {
        "id": "Rkgv_ver-QKu"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = le.transform(y_test)"
      ],
      "metadata": {
        "id": "kD0KYCVQ-Zx2"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "예측및 정확도 측정"
      ],
      "metadata": {
        "id": "7joe59wiC7Wz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test_con)\n",
        "acc = accuracy_score\n",
        "re = recall_score\n",
        "pr = precision_score\n",
        "f1 = f1_score\n",
        "\n",
        "print('accuracy_score : ', acc(y_test, y_pred))\n",
        "print('recall_score : ', re(y_test, y_pred, average='macro'))\n",
        "print('precision_score : ', pr(y_test, y_pred, average='macro'))\n",
        "print('f1_score : ', f1(y_test, y_pred, average='macro'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KU06ie53-hEq",
        "outputId": "cd671c12-a327-4592-eafe-f6611b5ab82f"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy_score :  0.9850746268656716\n",
            "recall_score :  0.989247311827957\n",
            "precision_score :  0.9761904761904763\n",
            "f1_score :  0.9821898401133374\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "파라미터 튜닝"
      ],
      "metadata": {
        "id": "MnK3jTNR_IfT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = {\n",
        "    'max_depth': [3,4,5],\n",
        "    'max_leaf_nodes':[5,6,7,8]\n",
        "}\n",
        "\n",
        "clf = DecisionTreeClassifier()\n",
        "grid = GridSearchCV(clf, param_grid=params, cv = 5, n_jobs=-1)\n",
        "grid.fit(X_train_con, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "4xwQbJ1pD9Kt",
        "outputId": "39307914-0bd2-4192-b8a1-fffd5f5704e5"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid={'max_depth': [3, 4, 5],\n",
              "                         'max_leaf_nodes': [5, 6, 7, 8]})"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid={&#x27;max_depth&#x27;: [3, 4, 5],\n",
              "                         &#x27;max_leaf_nodes&#x27;: [5, 6, 7, 8]})</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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid={&#x27;max_depth&#x27;: [3, 4, 5],\n",
              "                         &#x27;max_leaf_nodes&#x27;: [5, 6, 7, 8]})</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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HjV4MmAVE3s_",
        "outputId": "aff2e4dd-f7d0-40e8-fa90-2e532568f794"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 5, 'max_leaf_nodes': 8}"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "niNBjKauFHFh",
        "outputId": "a83f892f-7143-43e4-b33d-018b464724b5"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9699510831586304"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.best_estimator_.score(X_test_con, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ak1GuEwNFK08",
        "outputId": "02b6bc30-e5d9-4033-adc8-8c910e613037"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = DecisionTreeClassifier(max_depth = 5, max_leaf_nodes= 8)\n",
        "clf.fit(X_train_con, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "1ngK7siqFWxE",
        "outputId": "6c9d732c-9e69-467e-c0c2-7cbd065aaecb"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5, max_leaf_nodes=8)"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5, max_leaf_nodes=8)</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-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5, max_leaf_nodes=8)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle # binary로 저장\n",
        "with open('DT_penguins','wb') as file:\n",
        "  pickle.dump(clf,file)"
      ],
      "metadata": {
        "id": "Ennzp2ewGGLg"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('DT_penguins','rb') as file:\n",
        "  my_clf = pickle.load(file)"
      ],
      "metadata": {
        "id": "SNXOOjbxG9V-"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_clf.score(X_test_con, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1_6yObVHJvc",
        "outputId": "fb531753-bd6c-4807-cfa0-d848eac74f36"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('onehot_penguins','wb') as file:\n",
        "  pickle.dump(oh,file)\n",
        "with open('standard_penguins','wb') as file:\n",
        "  pickle.dump(ss,file)\n",
        "with open('label_penguins','wb') as file:\n",
        "  pickle.dump(le,file)"
      ],
      "metadata": {
        "id": "XICK1sMzHQp4"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('onehot_penguins','rb') as file:\n",
        "  my_oh = pickle.load(file)\n",
        "with open('standard_penguins','rb') as file:\n",
        "  my_ss = pickle.load(file)\n",
        "with open('label_penguins','rb') as file:\n",
        "  my_le = pickle.load(file)"
      ],
      "metadata": {
        "id": "qaIQrZHmJC9s"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MxDJekY4LD1f",
        "outputId": "e32ca1f7-d412-4631-e8d2-ed7aab7f3976"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['species', 'island', 'culmen_length_mm', 'culmen_depth_mm',\n",
              "       'flipper_length_mm', 'body_mass_g', 'sex'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = ['Torgersen', 38.9, 17.8, 181, 3625, 'FEMALE']\n",
        "data = pd.DataFrame(data).T\n",
        "data = data.rename(columns={0:'island', 1:'culmen_length_mm', 2:'culmen_depth_mm', 3:'flipper_length_mm', 4:'body_mass_g', 5:'sex'})\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "4G4yCZXdJPXs",
        "outputId": "4098e926-851d-450d-aa03-5b4e820a5087"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      island culmen_length_mm culmen_depth_mm flipper_length_mm body_mass_g  \\\n",
              "0  Torgersen             38.9            17.8               181        3625   \n",
              "\n",
              "      sex  \n",
              "0  FEMALE  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4cc3472b-93f0-4f7d-bf2e-def68b1323bd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>island</th>\n",
              "      <th>culmen_length_mm</th>\n",
              "      <th>culmen_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Torgersen</td>\n",
              "      <td>38.9</td>\n",
              "      <td>17.8</td>\n",
              "      <td>181</td>\n",
              "      <td>3625</td>\n",
              "      <td>FEMALE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4cc3472b-93f0-4f7d-bf2e-def68b1323bd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4cc3472b-93f0-4f7d-bf2e-def68b1323bd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4cc3472b-93f0-4f7d-bf2e-def68b1323bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['island'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mw20lp6oMo6g",
        "outputId": "c3aefcea-163d-4517-ce7e-98d8934da138"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Torgersen', 'Biscoe', 'Dream'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uGQlGwN5NCB5",
        "outputId": "119cc196-c5e3-4793-a0e4-27fb03fdeeab"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  species     island  culmen_length_mm  culmen_depth_mm  flipper_length_mm  \\\n",
              "0  Adelie  Torgersen              39.1             18.7              181.0   \n",
              "1  Adelie  Torgersen              39.5             17.4              186.0   \n",
              "2  Adelie  Torgersen              40.3             18.0              195.0   \n",
              "3  Adelie  Torgersen               NaN              NaN                NaN   \n",
              "4  Adelie  Torgersen              36.7             19.3              193.0   \n",
              "\n",
              "   body_mass_g     sex  \n",
              "0       3750.0    MALE  \n",
              "1       3800.0  FEMALE  \n",
              "2       3250.0  FEMALE  \n",
              "3          NaN     NaN  \n",
              "4       3450.0  FEMALE  "
            ],
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              "      <th>culmen_depth_mm</th>\n",
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              "      <td>Adelie</td>\n",
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              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
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              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
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              "      <td>3800.0</td>\n",
              "      <td>FEMALE</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>FEMALE</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>FEMALE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      background-color: #E8F0FE;\n",
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              "      <script>\n",
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              "          document.querySelector('#df-d8d9ede4-875d-4ce2-a6b8-1f277dff6bb4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d8d9ede4-875d-4ce2-a6b8-1f277dff6bb4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_oh.transform(data[['island','sex']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iq7DAxk2Jtxp",
        "outputId": "8ba3ee92-0082-4e05-de30-e27f8f4f088e"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 1., 1., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ss.transform(X_test[num_cols])"
      ],
      "metadata": {
        "id": "VLIS3zQjJ4h8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}