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UID:6618@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20201130T140000
DTEND;TZID=Europe/Paris:20201130T160000
DTSTAMP:20241120T201754Z
URL:https://www.i2m.univ-amu.fr/evenements/marianne-clausel-a-smooth-consi
 stent-regression-tree-and-ensemble-extensions-through-rf-and-gbt/
SUMMARY:Marianne Clausel (IECL\, University of Lorraine): Marianne Clausel 
 : A smooth\, consistent regression tree and ensemble extensions through RF
  and GBT
DESCRIPTION:Marianne Clausel: A smooth\, consistent regression tree and ens
 emble extensions through RF and GBT\nJoint work with S. Alkhoury (LIG)\, E
 . Devijver (LIG) and E. Gaussier (LIG)\nTree-based ensemble methods\, as R
 andom 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 cons
 iders that an observation\, even though it belongs to a particular region\
 , can still be associated to other regions with a certain weight that depe
 nds on the distance between the observation and the region. This generaliz
 ation raises several difficult questions\, in particular regarding consist
 ency. We show here that smooth trees are indeed consistent\, a property th
 at has not been established\, as far\nas we know\, on previous proposals a
 s soft trees.\nWe then show how smooth regression trees can be used in dif
 ferent ensemble methods\, namely Random Forests and Gradient Boosted Trees
 . Experiments conducted on several data sets further illustrate the good b
 ehavior of smooth trees and their ensemble extensions.\n
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 020/10/Marianne_Clausel.jpg
CATEGORIES:Séminaire,Statistique
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