# A study of tree-based methods and their combination

arXiv: https://arxiv.org/abs/2204.13916 · HTML: https://mlsanity.com/paper/2204.13916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 1 of 1)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)

## Built on by (most cited first, 1 of 1)

- [Classic algorithms are fair learners: Classification Analysis of natural weather and wildfire occurrences](https://mlsanity.com/paper/2309.01381/) (2023)

## Abstract (arXiv)

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.