# Trees-Based Models for Correlated Data

arXiv: https://arxiv.org/abs/2102.08114 · HTML: https://mlsanity.com/paper/2102.08114/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [Cross-Validation for Correlated Data](https://mlsanity.com/paper/1904.02438/) (2019)
- [Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance](https://mlsanity.com/paper/1512.03444/) (2015)

## Built on by (most cited first, 3 of 3)

- [Latent Gaussian Model Boosting](https://mlsanity.com/paper/2105.08966/) (2021)
- [Random forests for binary geospatial data](https://mlsanity.com/paper/2302.13828/) (2023)
- [Improving Group Lasso for high-dimensional categorical data](https://mlsanity.com/paper/2210.14021/) (2022)

## Abstract (arXiv)

This paper presents a new approach for trees-based regression, such as simple regression tree, random forest and gradient boosting, in settings involving correlated data. We show the problems that arise when implementing standard trees-based regression models, which ignore the correlation structure. Our new approach explicitly takes the correlation structure into account in the splitting criterion, stopping rules and fitted values in the leaves, which induces some major modifications of standard methodology. The superiority of our new approach over trees-based models that do not account for the correlation is supported by simulation experiments and real data analyses.