title: Class probability estimation for medical studies.
abstract: 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.
authors.affiliation: Biometric Research Branch, National Cancer Institute, 9609 Medical Park Drive, Rockville, MD, 20892-9735, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Simon
mesh_terms: D001185:Artificial Intelligence; D035843:Biomedical Research / Q000379:methods*; D001699:Biometry / Q000379:methods*; D011336:Probability
keywords: Logistic regression; Machine learning; Prediction