## SPORF

SPORF (Sparse Projection Oblique Randomer Forests) combines sparse random projections with the random forest algorithm to achieve high accuracy on a variety of datasets.

Currently available for R and Python, SPORF is supported on Windows, Linux, and Mac OS.

SPORF is optimized for both speed and memory performance through native implementation and multicore parallelization.

###### Publications

- 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.