A study of tree-based methods and their combination
Machine Learning
2022-05-02 v1 Machine Learning
Abstract
Tree-based methods are popular machine learning techniques used in various fields. In this work, we review their foundations and a general framework the importance sampled learning ensemble (ISLE) that accelerates their fitting process. Furthermore, we describe a model combination strategy called the adaptive regression by mixing (ARM), which is feasible for tree-based methods via ISLE. Moreover, three modified ISLEs are proposed, and their performance are evaluated on the real data sets.
Cite
@article{arxiv.2204.13916,
title = {A study of tree-based methods and their combination},
author = {Yinuo Zeng},
journal= {arXiv preprint arXiv:2204.13916},
year = {2022}
}