A working guide to boosted regression trees
The University of Melbourne · National Institute of Water and Atmospheric Research · +1 more institution
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
- FWCI
- 70.72
- Percentile
- 100%
- References
- 43
Authors
3Topics & keywords
- Boosting (machine learning)
- Computer science
- Regression
- Decision tree
- Outlier
- Machine learning
- Regression analysis
- Tree (set theory)