python-scikit-learnAdaboost classifier model learning rate
A higher learning rate increases the contribution of each classifier:
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.AdaBoostClassifier(learning_rate=10)
model.fit(X_train, y_train)
score = model.score(X_test, y_test)ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
model_selection.train_test_splitsplits given | .AdaBoostClassifier(create AdaBoost model |
.fit(train model with a given features and target variable dataset | learning_rateset learning rate (to |
.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.AdaBoostClassifier(learning_rate=10)
model.fit(X_train, y_train)
print(model.score(X_test, y_test))output
0.631578947368421
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