Recipe: How to use CatBoost Classifier and Regressor in Python?
MACHINE LEARNING RECIPES

How to use CatBoost Classifier and Regressor in Python?

This recipe helps you use CatBoost Classifier and Regressor in Python
In [2]:
## How to use CatBoost Classifier and Regressor in Python
def Snippet_168():
    print()
    print(format('How to use CatBoost Classifier and Regressor in Python','*^82'))
    import warnings
    warnings.filterwarnings("ignore")
    # load libraries
    from sklearn import datasets
    from sklearn import metrics
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt
    import seaborn as sns

    plt.style.use('ggplot')

    import catboost as ctb
    # load the iris datasets
    dataset = datasets.load_breast_cancer()
    X = dataset.data; y = dataset.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

    # fit a CART model to the data
    model = ctb.CatBoostClassifier()
    model.fit(X_train, y_train)
    print(); print(model)
    # make predictions
    expected_y  = y_test
    predicted_y = model.predict(X_test)
    # summarize the fit of the model
    print(); print(metrics.classification_report(expected_y, predicted_y))
    print(); print(metrics.confusion_matrix(expected_y, predicted_y))

    # load the boston datasets
    dataset = datasets.load_boston()
    X = dataset.data; y = dataset.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
    # fit a CART model to the data
    model = ctb.CatBoostRegressor()
    model.fit(X_train, y_train)
    print(); print(model)
    # make predictions
    expected_y  = y_test
    predicted_y = model.predict(X_test)
    # summarize the fit of the model
    print(); print(metrics.r2_score(expected_y, predicted_y))
    print(); print(metrics.mean_squared_log_error(expected_y, predicted_y))
    plt.figure(figsize=(10,10))
    sns.regplot(expected_y, predicted_y, fit_reg=True, scatter_kws={"s": 100})
Snippet_168()
**************How to use CatBoost Classifier and Regressor in Python**************
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<catboost.core.CatBoostClassifier object at 0x10f5c5fd0>

              precision    recall  f1-score   support

           0       0.92      0.98      0.95        49
           1       0.99      0.96      0.97        94

   micro avg       0.97      0.97      0.97       143
   macro avg       0.96      0.97      0.96       143
weighted avg       0.97      0.97      0.97       143


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<catboost.core.CatBoostRegressor object at 0x1a17fade80>

0.8788702647396355

0.02031622842099923


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