A practical guide to model selection
Isabelle Guyon
In: Proceedings of the machine learning summer school Springer Text in Statistics . (2009) Springer .

Abstract

This chapter is dedicated to students and practitioners who are new to the field of machine learning or data mining and want to: - quickly get results on some applications of interest, - understand the basic principles of the methods available to eventually be able to customize them, and - gain enough vocabulary and concepts to be able to read papers addressing more advanced topics. Slides accompanying this material are available at http://clopinet.com/isabelle/ Projects/MLSS08/. We focus in the chapter on the problem of model selection. The rest of the topics covered in the class are developed in tutorials on feature selection and causality by the same author.

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