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CATEGORIES:Statistics
SUMMARY:Random Planted Forest: a directly interpretable tr
 ee ensemble - Enno Mammen (Heidelberg University)
DTSTART;TZID=Europe/London:20220304T140000
DTEND;TZID=Europe/London:20220304T150000
UID:TALK168119AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/168119
DESCRIPTION:We introduce a novel interpretable and tree-based 
 algorithm for prediction in a regression setting i
 n which each tree in a classical random forest is 
 replaced by a family of planted trees that grow si
 multaneously.\nThe motivation for our algorithm is
  to estimate the unknown regression function from 
 a functional ANOVA decomposition perspective\, whe
 re each tree corresponds to a function within that
  decomposition.\nTherefore\, planted trees are lim
 ited in the number of interaction terms.  The maxi
 mal order of approximation in the ANOVA decomposit
 ion can be specified or left unlimited. If a first
  order approximation is chosen\, the result is an 
 additive model. In the other extreme case\, if the
  order of approximation is not limited\, the resul
 ting model places no restrictions on the form of t
 he regression function. In a simulation study we f
 ind encouraging prediction and visualisation prope
 rties of our  random planted forest method.\nWe al
 so develop theory for an idealised version of rand
 om planted forests. In particular\, for an additiv
 e model we show that the idealised version achieve
 s asymptotically optimal one-dimensional convergen
 ce rates of order $n^{-2/5}$ up to a logarithmic f
 actor. The talk reports on joint work with Munir H
 iabu (Copenhagen) and Joseph Theo Meyer (Heidelber
 g).
LOCATION:https://maths-cam-ac-uk.zoom.us/j/93998865836?pwd=
 VzVzN1VFQ0xjS3VDdlY0enBVckY5dz09
CONTACT:Qingyuan Zhao
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