Fru-arrow
Link to preprint | Pyfru docs | R version
Fru-arrow is a highly performant implementation of the Random Forest model. It uses Arrow PyCapsule underneath,
making integration with any library that supports it - polars, pandas, pyarrow straightforward.
Moreover, it features permutation importance with a novel, highly optimized algorithm.
It can be used for both classification and regression, as well as out-of-bag predictions.
Fru is typically anywhere from a few times to several thousand times faster than scikit-learn's Random Forest implementation. The performance gap widens as the number of threads increases.
The plot below illustrates this difference.
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Release files for pyfru 0.1.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
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| pyfru-0.1.1.tar.gz | 70.7 kB | Without provenance | Details |
Built distributions (wheels)
Total release size: 9.0 MB
Release files / pyfru-0.1.1.tar.gz
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Release files / pyfru-0.1.1-cp314-cp314t-win_amd64.whl
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Release files / pyfru-0.1.1-cp314-cp314t-musllinux_1_2_x86_64.whl
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Release files / pyfru-0.1.1-cp314-cp314t-musllinux_1_2_aarch64.whl
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