Narrowing the Gap: Random Forests In Theory and In Practice

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Misha Denil, David Matheson, Nando De Freitas ;
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):665-673, 2014.

Abstract

Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoreti- cally tractable variant of random regression forests and prove that our algorithm is con- sistent. We also provide an empirical eval- uation, comparing our algorithm and other theoretically tractable random forest models to the random forest algorithm used in prac- tice. Our experiments provide insight into the relative importance of different simplifi- cations that theoreticians have made to ob- tain tractable models for analysis.

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