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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 GitHub
parent 4fd95272c8
commit 8dfe7b3686
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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 * 4:output feature contributions to individual predictions
* \param ntree_limit limit number of trees used for prediction, this is only valid for boosted trees * \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 * 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_len used to store length of returning result
* \param out_result used to set a pointer to array * \param out_result used to set a pointer to array
* \return 0 when success, -1 when failure happens * \return 0 when success, -1 when failure happens

2
src/c_api/c_api.cc
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@ -426,7 +426,7 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
DMatrixHandle dmat, DMatrixHandle dmat,
int option_mask, int option_mask,
unsigned ntree_limit, unsigned ntree_limit,
int32_t training, int training,
xgboost::bst_ulong *len, xgboost::bst_ulong *len,
const bst_float **out_result) { const bst_float **out_result) {
API_BEGIN(); API_BEGIN();