Randomer Forest

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.

Publications
  1. R. Guo, C. Shen and J. T. Vogelstein. Estimating Information-Theoretic Quantities with Random Forests. arXiv, 2019.
  2. M. Madhyastha et al. Geodesic Learning via Unsupervised Decision Forests. arXiv, 2019.
  3. T. M. Tomita et al. Random Projection Forests. arXiv, 2018.
  4. J. Browne et al. Forest Packing: Fast, Parallel Decision Forests. SIAM International Conference on Data Mining, 2018.
  5. 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.