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[Blocking][jvm-packages] fix the early stopping feature (#3808)

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* add back train method but mark as deprecated

* add back train method but mark as deprecated

* add back train method but mark as deprecated

* add back train method but mark as deprecated

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* temp

* add method for classifier and regressor

* update tutorial

* address the comments

* update
This commit is contained in:
Nan Zhu
2018-10-23 14:53:13 -07:00
committed by GitHub
parent e26b5d63b2
commit 4ae225a08d
7 changed files with 134 additions and 14 deletions
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9
doc/jvm/xgboost4j_spark_tutorial.rst
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@@ -183,6 +183,15 @@ After we set XGBoostClassifier parameters and feature/label column, we can build
val xgbClassificationModel = xgbClassifier.fit(xgbInput)
Early Stopping
----------------
Early stopping is a feature to prevent the unnecessary training iterations. By specifying ``num_early_stopping_rounds`` or directly call ``setNumEarlyStoppingRounds`` over a XGBoostClassifier or XGBoostRegressor, we can define number of rounds for the evaluation metric going to the unexpected direction to tolerate before stopping the training.
In additional to ``num_early_stopping_rounds``, you also need to define ``maximize_evaluation_metrics`` or call ``setMaximizeEvaluationMetrics`` to specify whether you want to maximize or minimize the metrics in training.
After specifying these two parameters, the training would stop when the metrics goes to the other direction against the one specified by ``maximize_evaluation_metrics`` for ``num_early_stopping_rounds`` iterations.
Prediction
==========