scikit-learn-extra
A set of tools for scikit-learn.
Decision gist · record as of 2026-08-14
Yes, if you need specific algorithms beyond scikit-learn's core set and are comfortable with a smaller, less-mature project. The permissive BSD license, active maintenance, and scikit-learn-compatible API make it low-risk. However, the latest release was over a year ago—verify that the algorithms you need are stable and well-documented before adopting for critical production work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.6 and scikit-learn >=0.24 with its dependencies (numpy, scipy).
- Medium install friction due to compiled wheel dependencies across multiple Python versions and platforms.
- Repository is actively maintained with recent commits, though the latest release was over a year ago.
License · maintenance · safety
new BSD (permissive) — Released under new BSD, a permissive license that allows commercial and private use with minimal restrictions—suitable for most projects.
last release 2023-03-27 (1236 days) · last repo commit 2026-05-25 · 205 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 281,398 downloads/mo, #8,098 on PyPI
Alternatives
Verify before relying
pip install scikit-learn-extra
from scikit_learn_extra.cluster import KMedoids
import numpy as np
model = KMedoids(n_clusters=3)
model.fit(X)- Which specific algorithms are included and how they differ from scikit-learn's offerings.
- Performance characteristics and computational overhead compared to standard scikit-learn.
- Active user community size and support availability beyond the repository.
What it is and what it does
scikit-learn-extra is a Python extension module that adds machine learning algorithms to scikit-learn that are useful but fall outside scikit-learn's strict inclusion criteria—typically due to novelty or lower citation counts. It maintains API compatibility with scikit-learn, so algorithms work with the same fit/predict interface developers already know.
The package depends on numpy, scipy, and scikit-learn itself, making it a natural fit for projects already using the scikit-learn ecosystem. It provides pre-built wheels for multiple Python versions and platforms (macOS, Linux, Windows), though installation requires compilation support on some systems. The repository is actively maintained and has no known security vulnerabilities.
Use it for
- Access clustering algorithms when scikit-learn's built-in options don't fit your problem.
- Experiment with newer machine learning methods in production code while maintaining scikit-learn compatibility.
- Extend scikit-learn pipelines with additional estimators that follow the same interface conventions.
- Use algorithms from recent research papers that haven't yet reached scikit-learn's maturity threshold.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need specific algorithms beyond scikit-learn's core set and are comfortable with a smaller, less-mature project.
The permissive BSD license, active maintenance, and scikit-learn-compatible API make it low-risk. However, the latest release was over a year ago—verify that the algorithms you need are stable and well-documented before adopting for critical production work.
Install
scikit-learn-extra on PyPI
Before you install
Medium install friction due to compiled wheel dependencies across multiple Python versions and platforms. Repository is actively maintained with recent commits, though the latest release was over a year ago.
Requires Python >=3.6 and scikit-learn >=0.24 with its dependencies (numpy, scipy).
License in practice
Released under new BSD, a permissive license that allows commercial and private use with minimal restrictions—suitable for most projects.
Quickstart
pip install scikit-learn-extra
from scikit_learn_extra.cluster import KMedoids
import numpy as np
model = KMedoids(n_clusters=3)
model.fit(X)Verify before relying
- Which specific algorithms are included and how they differ from scikit-learn's offerings.
- Performance characteristics and computational overhead compared to standard scikit-learn.
- Active user community size and support availability beyond the repository.
Package facts
| License | new BSD permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesnumpyscipyscikit-learn |
| Maintenance | Actively maintained 1,236 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 281,398 / month, #8,098 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI ApprovedOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonTopic :: Scientific/EngineeringTopic :: Software Development |
Evidence: scikit_learn_extra-0.3.0-cp310-cp310-macosx_10_9_x86_64.whl; scikit_learn_extra-0.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; scikit_learn_extra-0.3.0-cp310-cp310-win_amd64.whl; scikit_learn_extra-0.3.0-cp311-cp311-macosx_10_9_x86_64.whl; scikit_learn_extra-0.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; scikit_learn_extra-0.3.0-cp311-cp311-win_amd64.whl; scikit_learn_extra-0.3.0-cp36-cp36m-macosx_10_9_x86_64.whl; scikit_learn_extra-0.3.0-cp36-cp36m-manylinux1_i686.whl; scikit_learn_extra-0.3.0-cp36-cp36m-manylinux1_x86_64.whl; scikit_learn_extra-0.3.0-cp36-cp36m-manylinux2010_i686.whl; scikit_learn_extra-0.3.0-cp36-cp36m-manylinux2010_x86_64.whl; scikit_learn_extra-0.3.0-cp36-cp36m-win32.whl; scikit_learn_extra-0.3.0-cp36-cp36m-win_amd64.whl; scikit_learn_extra-0.3.0-cp37-cp37m-macosx_10_9_x86_64.whl; scikit_learn_extra-0.3.0-cp37-cp37m-manylinux1_i686.whl; scikit_learn_extra-0.3.0-cp37-cp37m-manylinux1_x86_64.whl; scikit_learn_extra-0.3.0-cp37-cp37m-manylinux2010_i686.whl; scikit_learn_extra-0.3.0-cp37-cp37m-manylinux2010_x86_64.whl; scikit_learn_extra-0.3.0-cp37-cp37m-win32.whl; scikit_learn_extra-0.3.0-cp37-cp37m-win_amd64.whl
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See also imbalanced-learn · scikit-learn · sagemaker-scikit-learn-extension · forestci · azureml-train-core · array-api-extra · scikit-learn-intelex · hmmlearn · opentelemetry-instrumentation-sklearn · spark-sklearn