fru: A Blazing Fast Implementation of Random Forest
Abstract
Yet another implementation of the Random Forest method by Breiman (2001) , written in Rust and tailored towards stability, correctness, efficiency and scalability on modern multi-core machines. Handles both classification and regression, as well as provides permutation feature importance via a novel, highly optimised algorithm.
Fields of Study & Subjects
Statistical Methods and Inference
Machine Learning and Data Classification
Data Mining and Machine Learning Applications
Publication details
- Publication date
- 4 February 2026
- Document type
- Dataset