- XGBRegressor.predict(data, **kw)#
Predict with X. If the model is trained with early stopping, then
best_iterationis used automatically. The estimator uses inplace_predict by default and falls back to using
DMatrixif devices between the data and the estimator don’t match.
This function is only thread safe for gbtree and dart.
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
This docstring was copied from xgboost.sklearn.XGBRegressor.