A working guide to boosted regression trees
Article first published online: 8 APR 2008
© 2008 The Authors. Journal compilation © 2008 British Ecological Society
Journal of Animal Ecology
Volume 77, Issue 4, pages 802–813, July 2008
How to Cite
Elith, J., Leathwick, J. R. and Hastie, T. (2008), A working guide to boosted regression trees. Journal of Animal Ecology, 77: 802–813. doi: 10.1111/j.1365-2656.2008.01390.x
- Issue published online: 8 APR 2008
- Article first published online: 8 APR 2008
- Received 16 October 2007; accepted 25 January 2008Handling Editor: Bryan Manly
- data mining;
- machine learning;
- model averaging;
- random forests;
- species distributions
- 1Ecologists 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.
- 2This 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.
- 3Boosted 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.
- 4The 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.