# optuna.integration.XGBoostPruningCallback¶

class optuna.integration.XGBoostPruningCallback(trial, observation_key)[source]

Callback for XGBoost to prune unpromising trials.

See the example if you want to add a pruning callback which observes validation AUC of a XGBoost model.

Parameters
• trial – A Trial corresponding to the current evaluation of the objective function.

• observation_key – An evaluation metric for pruning, e.g., validation-error and validation-merror. When using the Scikit-Learn API, the index number of eval_set must be included in the observation_key, e.g., validation_0-error and validation_0-merror. Please refer to eval_metric in XGBoost reference for further details.