xorbits.xgboost.XGBRegressor.predict#

XGBRegressor.predict(data, **kw)[source]#

Predict with X. 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)) – Data to predict with.

  • output_margin ((Not supported yet)) – Whether to output the raw untransformed margin value.

  • 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.

    New in version 1.4.0(xgboost).

Return type

prediction

This docstring was copied from xgboost.sklearn.XGBRegressor.