Ensemble Models with Trees and Rules

TL;DR

This paper introduces post-processing techniques for large ensembles of models or rules, demonstrating improved prediction performance over existing methods like random forest and rulefit on benchmark data and simulations.

Contribution

It presents new post-processing approaches for ensemble models that enhance prediction accuracy compared to traditional ensemble methods.

Findings

Post-processing methods improved prediction performance.

Techniques outperformed random forest and rulefit in experiments.

Effective on benchmark and simulated datasets.

Abstract

In this article, we have proposed several approaches for post processing a large ensemble of prediction models or rules. The results from our simulations show that the post processing methods we have considered here are promising. We have used the techniques developed here for estimation of quantitative traits from markers, on the benchmark "Bostob Housing"data set and in some simulations. In most cases, the produced models had better prediction performance than, for example, the ones produced by the random forest or the rulefit algorithms.