Analysis of purely random forests bias - Archive ouverte HAL Access content directly
Reports (Research Report) Year : 2014

Analysis of purely random forests bias


Random forests are a very effective and commonly used statistical method, but their full theoretical analysis is still an open problem. As a first step, simplified models such as purely random forests have been introduced, in order to shed light on the good performance of random forests. In this paper, we study the approximation error (the bias) of some purely random forest models in a regression framework, focusing in particular on the influence of the number of trees in the forest. Under some regularity assumptions on the regression function, we show that the bias of an infinite forest decreases at a faster rate (with respect to the size of each tree) than a single tree. As a consequence, infinite forests attain a strictly better risk rate (with respect to the sample size) than single trees. Furthermore, our results allow to derive a minimum number of trees sufficient to reach the same rate as an infinite forest. As a by-product of our analysis, we also show a link between the bias of purely random forests and the bias of some kernel estimators.
Fichier principal
Vignette du fichier
hal_v1_purfbias.pdf (593.94 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01023596 , version 1 (14-07-2014)



Sylvain Arlot, Robin Genuer. Analysis of purely random forests bias. [Research Report] -. 2014. ⟨hal-01023596⟩
1403 View
456 Download



Gmail Facebook X LinkedIn More