Random Forests and Kernel Methods

Abstract : Random forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high-dimensional settings. On the theoretical side, several studies highlight the potentially fruitful connection between random forests and kernel methods. In this paper, we work out in full details this connection. In particular, we show that by slightly modifying their definition, random forests can be rewritten as kernel methods (called KeRF for Kernel based on Random Forests) which are more interpretable and easier to analyze. Explicit expressions of KeRF estimates for some specific random forest models are given, together with upper bounds on their rate of consistency. We also show empirically that KeRF estimates compare favourably to random forest estimates.
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Article dans une revue
IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2016, PP (99), pp.1-1. 〈10.1109/TIT.2016.2514489〉
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https://hal.sorbonne-universite.fr/hal-01255002
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Soumis le : mercredi 13 janvier 2016 - 09:49:23
Dernière modification le : dimanche 9 décembre 2018 - 01:23:37

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Erwan Scornet. Random Forests and Kernel Methods. IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2016, PP (99), pp.1-1. 〈10.1109/TIT.2016.2514489〉. 〈hal-01255002〉

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