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Beyond accuracy: Measures for assessing machine learning models, pitfalls and guidelines
Richard Dinga, Brenda W.J.H. Penninx, Dick J. Veltman, Lianne Schmaal, Andre F. Marquand
doi: https://doi.org/10.1101/743138
Richard Dinga
aDepartment of Psychiatry, Amsterdam UMC, Amsterdam, the Netherlands
bDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
Brenda W.J.H. Penninx
aDepartment of Psychiatry, Amsterdam UMC, Amsterdam, the Netherlands
Dick J. Veltman
aDepartment of Psychiatry, Amsterdam UMC, Amsterdam, the Netherlands
Lianne Schmaal
cOrygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia
dCentre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
Andre F. Marquand
bDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
Posted August 22, 2019.
Beyond accuracy: Measures for assessing machine learning models, pitfalls and guidelines
Richard Dinga, Brenda W.J.H. Penninx, Dick J. Veltman, Lianne Schmaal, Andre F. Marquand
bioRxiv 743138; doi: https://doi.org/10.1101/743138
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