Guyon, Isabelle; Saffari, Amir; Dror, Gideon; Cawley, Gavin Model selection: beyond the Bayesian/frequentist divide. (English) Zbl 1242.62008 J. Mach. Learn. Res. 11, 61-87 (2010). Summary: The principle of parsimony also known as “Ockham’s razor” has inspired many theories of model selection. Yet such theories, all making arguments in favor of parsimony, are based on very different premises and have developed distinct methodologies to derive algorithms. We have organized challenges and edited a special issue of JMLR and several conference proceedings around the theme of model selection. In this editorial, we revisit the problem of avoiding overfitting in light of the latest results. We note the remarkable convergence of theories as different as Bayesian theory, minimum description length, bias/variance tradeoff, structural risk minimization, and regularization, in some approaches. We also present new and interesting examples of the complementarity of theories leading to hybrid algorithms, neither frequentist, nor Bayesian, or perhaps both frequentist and Bayesian! Cited in 10 Documents MSC: 62A99 Foundational topics in statistics 62F15 Bayesian inference Keywords:ensemble methods; multilevel inference; multilevel optimization; performance prediction; bias-variance tradeoff; Bayesian priors; structural risk minimization; guaranteed risk minimization; over-fitting; regularization; minimum description length PDFBibTeX XMLCite \textit{I. Guyon} et al., J. Mach. Learn. Res. 11, 61--87 (2010; Zbl 1242.62008) Full Text: Link