Welcome to BoostForest’s documentation!

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.

Contents:

• Python Quick Start
• Parameters Tuning
• Python API
• Python Examples

References

[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, submitted, 2022.

[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.

Indices and tables

• Index