Randomer Forest combines sparse random projections with the random forest algorithm to achieve high accuracy on a variety of datasets.
Currently available for R and Python, Randomer Forest is supported on Windows, Linux, and Mac OS.
Randomer Forest is optimized for both speed and memory performance through native implementation and multicore parallelization.
- R. Guo, C. Shen and J. T. Vogelstein. Estimating Information-Theoretic Quantities with Random Forests. arXiv, 2019.
- M. Madhyastha et al. Geodesic Learning via Unsupervised Decision Forests. arXiv, 2019.
- T. M. Tomita et al. Random Projection Forests. arXiv, 2018.
- J. Browne et al. Forest Packing: Fast, Parallel Decision Forests. SIAM International Conference on Data Mining, 2018.
- T. Tomita, M. Maggioni and J. Vogelstein. ROFLMAO: Robust oblique forests with linear MAtrix operations. Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017, 2017.