python-scikit-learnUsing Gradient boosting classifier example

from sklearn import datasets, ensemble, model_selection

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = ensemble.GradientBoostingClassifier()
model.fit(X_train, y_train)

score = model.score(X_test, y_test)ctrl + cgithub

`from sklearn import` import module from scikit-learn	`load_iris` loads Iris dataset
`model_selection.train_test_split` splits given `X` and `y` datasets to test (25% of values by default) and train (75% of values by default) subsets	`.GradientBoostingClassifier(` creates gradient boosting classification model
`.fit(` train model with a given features and target variable dataset	`.score(` returns model accuracy score

Usage example

from sklearn import datasets, ensemble, model_selection

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = ensemble.GradientBoostingClassifier()
model.fit(X_train, y_train)

print(model.score(X_test, y_test))

output

0.9473684210526315

Using Gradient boosting regressor example

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python-scikit-learnUsing Gradient boosting classifier example

Usage example

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