Recipe: How to use GradientBoosting Classifier and Regressor in Python?
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How to use GradientBoosting Classifier and Regressor in Python?

This recipe helps you use GradientBoosting Classifier and Regressor in Python
In [2]:
## How to use GradientBoosting Classifier and Regressor in Python
def Snippet_164():
    print()
    print(format('How to use GradientBoosting Classifier and Regressor in Python','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import datasets
    from sklearn import metrics
    from sklearn.ensemble import GradientBoostingClassifier
    from sklearn.ensemble import GradientBoostingRegressor
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt
    import seaborn as sns

    plt.style.use('ggplot')

    # 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 = GradientBoostingClassifier()
    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 = GradientBoostingRegressor()
    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))

    # plot regression
    plt.figure(figsize=(10,10))
    sns.regplot(expected_y, predicted_y, fit_reg=True, scatter_kws={"s": 100})

Snippet_164()
**********How to use GradientBoosting Classifier and Regressor in Python**********

GradientBoostingClassifier(criterion='friedman_mse', init=None,
              learning_rate=0.1, loss='deviance', max_depth=3,
              max_features=None, max_leaf_nodes=None,
              min_impurity_decrease=0.0, min_impurity_split=None,
              min_samples_leaf=1, min_samples_split=2,
              min_weight_fraction_leaf=0.0, n_estimators=100,
              n_iter_no_change=None, presort='auto', random_state=None,
              subsample=1.0, tol=0.0001, validation_fraction=0.1,
              verbose=0, warm_start=False)

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        55
           1       1.00      1.00      1.00        88

   micro avg       1.00      1.00      1.00       143
   macro avg       1.00      1.00      1.00       143
weighted avg       1.00      1.00      1.00       143


[[55  0]
 [ 0 88]]

GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
             max_leaf_nodes=None, min_impurity_decrease=0.0,
             min_impurity_split=None, min_samples_leaf=1,
             min_samples_split=2, min_weight_fraction_leaf=0.0,
             n_estimators=100, n_iter_no_change=None, presort='auto',
             random_state=None, subsample=1.0, tol=0.0001,
             validation_fraction=0.1, verbose=0, warm_start=False)

0.8954727709411234

0.01882499632501215


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