Using and understanding cross-validation strategies. Perspectives on Saeb et al.
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.
Department of Mathematics, Astun University, Aston Triangle, B4 7ET, Birmingham, UK.
Max A
MA
Little
Parietal, INRIA, NeuroSpin, bat 145 CEA Saclay, 91191, Gif sur Yvette, France.
Gael
G
Varoquaux
Department of Preventive Medicine, Northwestern University, 750 N Lake Shore Dr, 60611, Chicago, USA.
Sohrab
S
Saeb
Rehabilitation Institute of Chicago, 345 E Superior, 60611, Chicago, USA.
Luca
L
Lonini
Rehabilitation Institute of Chicago, 345 E Superior, 60611, Chicago, USA.
Arun
A
Jayaraman
Department of Preventive Medicine, Northwestern University, 750 N Lake Shore Dr, 60611, Chicago, USA.
David C
DC
Mohr
Department of Preventive Medicine, Northwestern University, 750 N Lake Shore Dr, 60611, Chicago, USA.|Rehabilitation Institute of Chicago, 345 E Superior, 60611, Chicago, USA.
Konrad P
KP
Kording
D000069550:Machine Learning; D012107:Research Design
clinical applications; cross-validation; machine learning