A working guide to boosted regression trees
Open accessAbstract
1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
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Boosted trees and support vector regression have been studied for finding longitudinal predictors of glycaemic health.
“Also, Boosted trees (Elith et al., 2008) and SVR (support vector regression) (Awad and Khanna, 2015) have been investigated for finding the longitudinal predictors of glycaemic health (L et al., 2018).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Elith et al., 2008
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- Authors
- Elith, J., Leathwick, J. R., & Hastie, T.
- Affiliations
- The University of Melbourne (AU)
- National Institute of Water and Atmospheric Research (NZ)
- Stanford University (US)
- Corresponding author
- Elith, J.
- ORCID
- Venue
- Journal of Animal Ecology
- Publisher
- Wiley
- Published
- 2008-04-08
- Volume
- 77
- Issue
- 4
- Pages
- 802-813
- ISSN
- 0021-8790 (ISSN-L)
- 1365-2656
- OpenAlex
- W2135695572
- Cited by
- 6,484
- References
- 43
- Citations by year
- 2026: 412 · 2025: 569 · 2024: 557 · 2023: 588 · 2022: 623 · 2021: 640 · 2020: 589 · 2019: 497 · 2018: 397 · 2017: 350 · 2016: 295 · 2015: 265 · 2014: 207 · 2013: 162 · 2012: 118
- FWCI
- 37.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Animal Ecology (publishedVersion)
- PubMed (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research)
- Funders
- Australian Research Council (DP0772671, LP0667891)
- Topics
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- boosted regression trees, ensemble methods, regression trees, boosting, nonlinear relationship, interaction effects, ecological modeling, Anguilla australis, freshwater fish distribution, model interpretation, missing data
- MeSH terms
- Anguilla, Animals, Decision Trees, Ecology, Models, Biological, Regression Analysis, Statistics as Topic, Models, Statistical
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