Narrowing the Gap: Random Forests In Theory and In Practice

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v32-denil14, title = {Narrowing the Gap: Random Forests In Theory and In Practice}, author = {Denil, Misha and Matheson, David and De Freitas, Nando}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {665--673}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/denil14.pdf}, url = {https://proceedings.mlr.press/v32/denil14.html}, 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.} }
Endnote
%0 Conference Paper %T Narrowing the Gap: Random Forests In Theory and In Practice %A Misha Denil %A David Matheson %A Nando De Freitas %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-denil14 %I PMLR %P 665--673 %U https://proceedings.mlr.press/v32/denil14.html %V 32 %N 1 %X 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.
RIS
TY - CPAPER TI - Narrowing the Gap: Random Forests In Theory and In Practice AU - Misha Denil AU - David Matheson AU - Nando De Freitas BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/01/27 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-denil14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 1 SP - 665 EP - 673 L1 - http://proceedings.mlr.press/v32/denil14.pdf UR - https://proceedings.mlr.press/v32/denil14.html AB - 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. ER -
APA
Denil, M., Matheson, D. & De Freitas, N.. (2014). Narrowing the Gap: Random Forests In Theory and In Practice. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(1):665-673 Available from https://proceedings.mlr.press/v32/denil14.html.

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