BoostForest [1] is an ensemble learning approach that bases on model tree [2], boosting [3] and bootstrap aggregating (Bagging) [4]. It is designed to be efficient with the following advantages:
- Support of classification and regression in supervised learning.
- Support of achieving better generalization performance than traditional tree-based ensemble learning approaches.
Our primary documentation is at https://boostforest.readthedocs.io and is generated from this repository. If you are new to BoostForest, follow the installation instructions on that site.
Next you may want to read:
- APIs & Parameters is an exhaustive list of customization you can make.
- Parameters Tuning is an exhaustive list of customization you can make.
- Examples showing command line usage of common tasks.
[1] C. Zhao, D. Wu, J. Huang, Y. Yuan, H. Zhang, R. Peng and Z. Shi, “BoostTree and BoostForest for ensemble learning,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 2022, in press.
[2] Y. Wang and I. H. Witten, “Induction of model trees for predicting continuous classes,” in Proc. 9th European Conf. on Machine Learning, Prague, Czech Republic, 1997.
[3] J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.
[4] L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996.