Marianne Clausel : A smooth, consistent regression tree and ensemble extensions through RF and GBT

Marianne Clausel
IECL, University of Lorraine
https://sites.google.com/site/marianneclausel/

Date(s) : 30/11/2020   iCal
14 h 00 min - 16 h 00 min

A smooth, consistent regression tree and ensemble extensions through RF and GBT

Joint work with S. Alkhoury (LIG), E. Devijver (LIG) and E. Gaussier (LIG)

Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression problems in many applications and research studies. We propose here a generalization of regression trees, referred to as smooth trees, that adapt to the smoothness of the link function. By doing so, one considers that an observation, even though it belongs to a particular region, can still be associated to other regions with a certain weight that depends on the distance between the observation and the region. This generalization raises several difficult questions, in particular regarding consistency. We show here that smooth trees are indeed consistent, a property that has not been established, as far
as we know, on previous proposals as soft trees.
We then show how smooth regression trees can be used in different ensemble methods, namely Random Forests and Gradient Boosted Trees. Experiments conducted on several data sets further illustrate the good behavior of smooth trees and their ensemble extensions.

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