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Class probability estimation for medical studies
Author(s) -
Simon Richard
Publication year - 2014
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201300296
Subject(s) - estimation , class (philosophy) , artificial intelligence , machine learning , logistic regression , outcome (game theory) , mathematics , statistics , computer science , mathematical economics , economics , management
I provide a commentary on two papers “Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory” by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and “Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications” by Jochen Kruppa, Yufeng Liu, Hans‐Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. Those papers provide an up‐to‐date review of some popular machine learning methods for class probability estimation and compare those methods to logistic regression modeling in real and simulated datasets.