Scikit-Learn API

XGBRegressor

Constructor

XGBRegressor(*args, **kwargs)

Implementation of the scikit-learn API for XGBoost regression.

Attributes

XGBRegressor.apply(X[, iteration_range])	Return the predicted leaf every tree for each sample.
XGBRegressor.evals_result()	Return the evaluation results.
XGBRegressor.fit(X, y[, sample_weight, ...])	Fit gradient boosting model.
XGBRegressor.get_booster()	Get the underlying xgboost Booster of this model.
XGBRegressor.get_num_boosting_rounds()	Gets the number of xgboost boosting rounds.
XGBRegressor.get_params([deep])	Get parameters.
XGBRegressor.get_xgb_params()	Get xgboost specific parameters.
XGBRegressor.load_model(fname)	Load the model from a file or bytearray.
XGBRegressor.predict(data, **kw)	Predict with X.
XGBRegressor.save_model(fname)	Save the model to a file.
XGBRegressor.set_params(**params)	Set the parameters of this estimator.

XGBClassifier

Constructor

XGBClassifier(*args, **kwargs)

Implementation of the scikit-learn API for XGBoost classification.

Attributes

XGBClassifier.apply(X[, iteration_range])	Return the predicted leaf every tree for each sample.
XGBClassifier.evals_result()	Return the evaluation results.
XGBClassifier.fit(X, y[, sample_weight, ...])	Fit gradient boosting classifier.
XGBClassifier.get_booster()	Get the underlying xgboost Booster of this model.
XGBClassifier.get_num_boosting_rounds()	Gets the number of xgboost boosting rounds.
XGBClassifier.get_params([deep])	Get parameters.
XGBClassifier.get_xgb_params()	Get xgboost specific parameters.
XGBClassifier.load_model(fname)	Load the model from a file or bytearray.
XGBClassifier.predict(data, **kw)
XGBClassifier.predict_proba(data[, ntree_limit])	Predict the probability of each X example being of a given class.
XGBClassifier.save_model(fname)	Save the model to a file.
XGBClassifier.score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
XGBClassifier.set_params(**params)	Set the parameters of this estimator.