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Tree Ensembles with Rule Structured Horseshoe Regularization
[article]
2018
arXiv
pre-print
We propose a new Bayesian model for flexible nonlinear regression and classification using tree ensembles. The model is based on the RuleFit approach in Friedman and Popescu (2008) where rules from decision trees and linear terms are used in a L1-regularized regression. We modify RuleFit by replacing the L1-regularization by a horseshoe prior, which is well known to give aggressive shrinkage of noise predictor while leaving the important signal essentially untouched. This is especially
arXiv:1702.05008v2
fatcat:aczafxrbcfatbfodvgvf6ulasi