Tree-based ensembles for multi-output regression: Comparing multivariate approaches with separate univariate ones

Lena Schmid, Alexander Gerharz, Andreas Groll and Markus Pauly

Computational Statistics & Data Analysis, 2023, vol. 179, issue C

Abstract: Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods. In particular, they are used for predicting univariate responses. In case of multiple outputs the question arises whether it is better to separately fit univariate models or directly follow a multivariate approach. For the latter, several possibilities exist that are, e.g. based on modified splitting or stopping rules for multi-output regression. These methods are compared in extensive simulations and a real data example to help in answering the primary question when to use multivariate ensemble techniques instead of univariate ones.

Keywords: Machine learning; Multi-output regression; Multivariate trees (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947322002080
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:179:y:2023:i:c:s0167947322002080

DOI: 10.1016/j.csda.2022.107628

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().