Tuning parameters in random forests
Copyright policy )Tuning parameters in random forests
Summary: \textit{L. Breiman}'s [Mach. Learn. 45, No. 1, 5--32 (2001; Zbl 1007.68152)] random forests are a very popular class of learning algorithms often able to produce good predictions even in high-dimensional frameworks, with no need to accurately tune its inner parameters. Unfortunately, there are no theoretical findings to support the default values used for these parameters in Breiman's algorithm. The aim of this paper is therefore to present recent theoretical results providing some insights on the role and the tuning of these parameters.
- École Polytechnique France
T57-57.97, Applied mathematics. Quantitative methods, Classification and discrimination; cluster analysis (statistical aspects), QA1-939, Learning and adaptive systems in artificial intelligence, Mathematics
T57-57.97, Applied mathematics. Quantitative methods, Classification and discrimination; cluster analysis (statistical aspects), QA1-939, Learning and adaptive systems in artificial intelligence, Mathematics
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