xorbits.xgboost.XGBClassifier.predict_proba#

XGBClassifier.predict_proba(data, ntree_limit=None, **kw)[source]#

Predict the probability of each X example being of a given class. If the model is trained with early stopping, then best_iteration is used automatically. The estimator uses inplace_predict by default and falls back to using DMatrix if devices between the data and the estimator don’t match.

Note

This function is only thread safe for gbtree and dart.

Parameters

  • X ((Not supported yet)) – Feature matrix. See py-data for a list of supported types.

  • validate_features ((Not supported yet)) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

  • base_margin ((Not supported yet)) – Margin added to prediction.

  • iteration_range ((Not supported yet)) – Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying iteration_range=(10, 20), then only the forests built during [10, 20) (half open set) rounds are used in this prediction.

Returns

a numpy array of shape array-like of shape (n_samples, n_classes) with the probability of each data example being of a given class.

Return type

prediction

This docstring was copied from xgboost.sklearn.XGBClassifier.