articleJournal of Animal EcologyApr 8, 2008BRONZE OA

A working guide to boosted regression trees

The University of Melbourne · National Institute of Water and Atmospheric Research · +1 more institution

PubMed
Indexed incrossrefdatacitepubmed

Abstract

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…

Citation impact

6,426
total citations
FWCI
70.72
Percentile
100%
References
43
Citations per year

Authors

3

Topics & keywords

Keywords
  • Boosting (machine learning)
  • Computer science
  • Regression
  • Decision tree
  • Outlier
  • Machine learning
  • Regression analysis
  • Tree (set theory)
No related works found for this paper.