Using and understanding cross-validation strategies Perspectives on Saeb et al

被引：105

作者：

Little, Max A. ^{[1
]}

Varoquaux, Gael ^{[2
]}

Saeb, Sohrab ^{[3
]}

Lonini, Luca ^{[4
]}

Jayaraman, Arun ^{[4
]}

Mohr, David C. ^{[3
]}

Kording, Konrad P. ^{[3
,4
]}

机构：

[1] Astun Univ, Dept Math, Birmingham B4 7ET, W Midlands, England

[2] CEA Saclay, Parietal, NeuroSpin, INRIA, Bat 145, F-91191 Gif Sur Yvette, France

[3] Northwestern Univ, Dept Prevent Med, 750 N Lake Shore Dr, Chicago, IL 60611 USA

[4] Rehabil Inst Chicago, 345 E Super, Chicago, IL 60611 USA

来源：

GIGASCIENCE | 2017年 / 6卷 / 05期

关键词：

machine learning; clinical applications; cross-validation;

D O I：

10.1093/gigascience/gix020

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This three-part review takes a detailed look at the complexities of cross-validation, fostered by the peer review of Saeb et al.'s paper entitled "The need to approximate the use-case in clinical machine learning." It contains perspectives by reviewers and by the original authors that touch upon cross-validation: the suitability of different strategies and their interpretation.

引用

页数：6

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