bcc0277338
* Re-implement ROC-AUC. * Binary * MultiClass * LTR * Add documents. This PR resolves a few issues: - Define a value when the dataset is invalid, which can happen if there's an empty dataset, or when the dataset contains only positive or negative values. - Define ROC-AUC for multi-class classification. - Define weighted average value for distributed setting. - A correct implementation for learning to rank task. Previous implementation is just binary classification with averaging across groups, which doesn't measure ordered learning to rank.
5 lines
127 B
Plaintext
5 lines
127 B
Plaintext
/*!
|
|
* Copyright 2021 XGBoost contributors
|
|
*/
|
|
// Dummy file to keep the CUDA conditional compile trick.
|
|
#include "test_auc.cc" |