title: Risk prediction with machine learning and regression methods.
abstract: This is a discussion of issues in risk prediction based on the following 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.
authors.affiliation: Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
authors.forename: Tjeerd
authors.initials: T
authors.lastname: van der Ploeg
authors.forename: Ben
authors.initials: B
authors.lastname: Van Calster
mesh_terms: D001185:Artificial Intelligence; D035843:Biomedical Research; D001699:Biometry; D006801:Humans; D011336:Probability; D012044:Regression Analysis; D018570:Risk Assessment; D060388:Support Vector Machine; D035501:Uncertainty
keywords: Machine learning; Prediction; Regression