scikit-multilearn
Scikit-multilearn is a BSD-licensed library for multi-label classification that is built on top of the well-known scikit-learn ecosystem.
Decision gist · record as of 2026-08-14
No, unless you are maintaining legacy code. The package is abandoned (last release 2018-12-10) and will not receive updates for modern Python or dependency versions. For new multi-label classification projects, consider actively maintained alternatives. If you must use it, pin all dependencies to versions known to work with scikit-multilearn 0.2.0 and test thoroughly.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy, scipy, scikit-learn, and other dependencies; some optional clusterers require GPL-licensed packages.
- Installation is low-friction with pure Python wheels available.
- However, the package is abandoned—last release was 2018-12-10.
License · maintenance · safety
BSD (permissive) — BSD license is permissive and poses no restrictions on commercial or private use, modification, or redistribution.
last release 2018-12-10 (2804 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 77,746 downloads/mo, #14,500 on PyPI
Alternatives
Verify before relying
pip install scikit-multilearn
from skmultilearn.problem_transform import BinaryRelevance
classifier = BinaryRelevance(require_dense=[False,True])
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)- Whether the package works with current versions of scikit-learn and numpy given its 2018 release date.
- Compatibility with modern Python versions not specified in the fact sheet.
- Whether the MEKA wrapper and other integrations remain functional without active maintenance.
- Current state of optional GPL-licensed dependencies (python-igraph, python-graphtool) and their installation complexity.
What it is and what it does
Scikit-multilearn is a Python library for multi-label classification—problems where each sample can belong to multiple classes simultaneously. It implements problem-transformation methods and algorithm-adaptation approaches, built on top of numpy and scipy with an API modeled after scikit-learn. The library also provides a wrapper for MEKA, MULAN, and WEKA, and includes community-detection-based clusterers for label correlation.
The project has been abandoned since 2018-12-10 with no active maintenance, meaning it will not receive updates for compatibility with newer Python or dependency versions, nor any security patches. Installation is low-friction with pure Python wheels, but long-term viability for new projects is questionable.
Use it for
- Text categorization where documents can belong to multiple topics simultaneously.
- Image tagging where a single image can have multiple relevant labels or tags.
- Biological sequence annotation where genes or proteins may have multiple functional roles.
- Recommendation systems where items can belong to multiple categories or user interests.
- Medical diagnosis where a patient may have multiple concurrent conditions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No, unless you are maintaining legacy code.
The package is abandoned (last release 2018-12-10) and will not receive updates for modern Python or dependency versions. For new multi-label classification projects, consider actively maintained alternatives. If you must use it, pin all dependencies to versions known to work with scikit-multilearn 0.2.0 and test thoroughly.
Install
scikit-multilearn on PyPI
Before you install
Installation is low-friction with pure Python wheels available. However, the package is abandoned—last release was 2018-12-10. No active maintenance or security updates should be expected.
Requires numpy, scipy, scikit-learn, and other dependencies; some optional clusterers require GPL-licensed packages.
License in practice
BSD license is permissive and poses no restrictions on commercial or private use, modification, or redistribution.
Quickstart
pip install scikit-multilearn
from skmultilearn.problem_transform import BinaryRelevance
classifier = BinaryRelevance(require_dense=[False,True])
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)Verify before relying
- Whether the package works with current versions of scikit-learn and numpy given its 2018 release date.
- Compatibility with modern Python versions not specified in the fact sheet.
- Whether the MEKA wrapper and other integrations remain functional without active maintenance.
- Current state of optional GPL-licensed dependencies (python-igraph, python-graphtool) and their installation complexity.
Package facts
| License | BSD permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Abandoned 2,804 days since the last release |
| First released | |
| Downloads | 77,746 / month, #14,500 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIXProgramming Language :: PythonTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Bio-InformaticsTopic :: Scientific/Engineering :: Information Analysis |
Evidence: scikit_multilearn-0.2.0-py2-none-any.whl; scikit_multilearn-0.2.0-py3-none-any.whl
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