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Clarify meaning of training parameter in XGBoosterPredict() (#5604)

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Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
Jason E. Aten, Ph.D
2020-04-25 19:48:42 -04:00
committed by Hyunsu Cho
parent 636ab6b522
commit afa6e086cc
2 changed files with 9 additions and 2 deletions
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9
include/xgboost/c_api.h
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@@ -418,7 +418,14 @@ XGB_DLL int XGBoosterEvalOneIter(BoosterHandle handle,
* 4:output feature contributions to individual predictions
* \param ntree_limit limit number of trees used for prediction, this is only valid for boosted trees
* when the parameter is set to 0, we will use all the trees
* \param training Whether the prediction value is used for training.
* \param training Whether the prediction function is used as part of a training loop.
* Prediction can be run in 2 scenarios:
* 1. Given data matrix X, obtain prediction y_pred from the model.
* 2. Obtain the prediction for computing gradients. For example, DART booster performs dropout
* during training, and the prediction result will be different from the one obtained by normal
* inference step due to dropped trees.
* Set training=false for the first scenario. Set training=true for the second scenario.
* The second scenario applies when you are defining a custom objective function.
* \param out_len used to store length of returning result
* \param out_result used to set a pointer to array
* \return 0 when success, -1 when failure happens