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| -rw-r--r-- | gnu/packages/machine-learning.scm | 29 |
1 files changed, 29 insertions, 0 deletions
diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm index 9a88b53415..223d03e979 100644 --- a/gnu/packages/machine-learning.scm +++ b/gnu/packages/machine-learning.scm @@ -867,6 +867,35 @@ data analysis.") (base32 "08zbzi8yx5wdlxfx9jap61vg1malc9ajf576w7a0liv6jvvrxlpj"))))))) +(define-public python-scikit-rebate + (package + (name "python-scikit-rebate") + (version "0.6") + (source (origin + (method url-fetch) + (uri (pypi-uri "skrebate" version)) + (sha256 + (base32 + "1h7qs9gjxpzqabzhb8rmpv3jpmi5iq41kqdibg48299h94iikiw7")))) + (build-system python-build-system) + ;; Pandas is only needed to run the tests. + (native-inputs + `(("python-pandas" ,python-pandas))) + (propagated-inputs + `(("python-numpy" ,python-numpy) + ("python-scipy" ,python-scipy) + ("python-scikit-learn" ,python-scikit-learn) + ("python-joblib" ,python-joblib))) + (home-page "https://epistasislab.github.io/scikit-rebate/") + (synopsis "Relief-based feature selection algorithms for Python") + (description "Scikit-rebate is a scikit-learn-compatible Python +implementation of ReBATE, a suite of Relief-based feature selection algorithms +for Machine Learning. These algorithms excel at identifying features that are +predictive of the outcome in supervised learning problems, and are especially +good at identifying feature interactions that are normally overlooked by +standard feature selection algorithms.") + (license license:expat))) + (define-public python-autograd (let* ((commit "442205dfefe407beffb33550846434baa90c4de7") (revision "0") |