Nested cross-validation when selecting classifiers is overzealous for most practical applications
作者：
Highlights：
• Flat cross validation computes both the best hyperparameter and the expected accuracy.
• Nested cross validation separated both computations and it is more costly.
• Nested cross validation does not incur on biased estimation of the accuracy.
• Algorithm selection using flat cross validation does not incur in worse selections.
摘要
•Flat cross validation computes both the best hyperparameter and the expected accuracy.•Nested cross validation separated both computations and it is more costly.•Nested cross validation does not incur on biased estimation of the accuracy.•Algorithm selection using flat cross validation does not incur in worse selections.
论文关键词：Hyperparameters,Classification,cross-validation,Nested cross-validation,Model selection
论文评审过程：Received 21 December 2019, Revised 16 March 2021, Accepted 14 May 2021, Available online 17 May 2021, Version of Record 19 May 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115222
