A new Rust-based implementation of Random Forest, named Fru, has been developed and published in the Software X journal. This implementation offers significant performance improvements and better scalability compared to existing popular libraries. Fru achieves several times the speed of scikit-learn in Python and is typically dozens of percent faster than R's ranger package, with potential speedups reaching several times faster in specific use cases. The project also introduces a novel permutation importance implementation and provides bindings for both Python and R, integrating seamlessly with libraries like pandas and polars. AI
IMPACT Offers significant performance gains for Random Forest implementations, potentially accelerating machine learning workflows.
RANK_REASON Publication of a new software implementation in a journal. [lever_c_demoted from research: ic=1 ai=0.7]
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