Tree Ensembles with Rule Structured Horseshoe Regularization
This work addresses the challenge of noise reduction and model interpretability in tree ensembles for researchers and practitioners in machine learning, representing an incremental improvement over existing methods.
The authors tackled the problem of improving tree ensemble models for regression and classification by replacing L1-regularization in RuleFit with a horseshoe prior that aggressively shrinks noise predictors, especially for complex or rare rules, and incorporating additional trees from random forest for diversity. The result was a new Bayesian model that outperformed state-of-the-art methods like RuleFit, BART, and random forest on 16 datasets, with concrete performance gains demonstrated.
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 important when a large number of rules are used as predictors as many of them only contribute noise. Our horseshoe prior has an additional hierarchical layer that applies more shrinkage a priori to rules with a large number of splits, and to rules that are only satisfied by a few observations. The aggressive noise shrinkage of our prior also makes it possible to complement the rules from boosting in Friedman and Popescu (2008) with an additional set of trees from random forest, which brings a desirable diversity to the ensemble. We sample from the posterior distribution using a very efficient and easily implemented Gibbs sampler. The new model is shown to outperform state-of-the-art methods like RuleFit, BART and random forest on 16 datasets. The model and its interpretation is demonstrated on the well known Boston housing data, and on gene expression data for cancer classification. The posterior sampling, prediction and graphical tools for interpreting the model results are implemented in a publicly available R package.