scikit-learn
A set of python modules for machine learning and data mining
Decision gist · record as of 2026-08-14
Yes. scikit-learn is a foundational, actively maintained library with no known vulnerabilities, broad platform coverage, and permissive licensing. It is the de facto standard for classical machine learning in Python. Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.11; NumPy, SciPy, joblib, narwhals, and threadpoolctl must be installed.
- Medium install friction due to compiled C extensions and numerical dependencies (NumPy, SciPy), but pre-built wheels cover all major platforms and Python versions (3.11–3.14).
- Active maintenance with releases every 73 days on average.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
last release 2026-06-02 (73 days) · last repo commit 2026-08-13 · 66,975 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 235,451,583 downloads/mo, #171 on PyPI
Alternatives
Verify before relying
pip install scikit-learn
import scikit-learn
from numpy import array
from scipy import stats- Whether plotting functions (plot_* and *Display classes) work without explicitly installing Matplotlib as a separate dependency
- Performance characteristics for large datasets or specific algorithm families
- Specific algorithms and model types included in the library beyond what the description excerpt covers
What it is and what it does
scikit-learn is a production-grade machine learning library that provides a unified API for training, evaluating, and deploying supervised and unsupervised learning models. It wraps efficient implementations of classical algorithms and exposes them through a consistent Python interface. The library handles data preprocessing, feature selection, model validation, and cross-validation workflows.
The package depends on NumPy for array operations, SciPy for scientific computing, joblib for parallelization, narwhals for dataframe interoperability, and threadpoolctl for thread management. It is actively maintained by volunteers and has been in production use since 2011. The library is designed for practitioners and researchers who need to build, tune, and validate machine learning pipelines without implementing algorithms from scratch.
Use it for
- Train and evaluate supervised learning models on labeled datasets.
- Perform unsupervised learning tasks like clustering and dimensionality reduction.
- Build ensemble models combining multiple learners for improved predictions.
- Preprocess and transform data as part of a machine learning pipeline.
- Conduct hyperparameter tuning and model selection via grid search and cross-validation.
- Extract feature importance and model interpretability metrics for explainability.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
scikit-learn is a foundational, actively maintained library with no known vulnerabilities, broad platform coverage, and permissive licensing. It is the de facto standard for classical machine learning in Python. Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
Install
scikit-learn on PyPI
Before you install
Medium install friction due to compiled C extensions and numerical dependencies (NumPy, SciPy), but pre-built wheels cover all major platforms and Python versions (3.11–3.14). Active maintenance with releases every 73 days on average.
Requires Python >= 3.11; NumPy, SciPy, joblib, narwhals, and threadpoolctl must be installed.
License in practice
BSD-3-Clause permissive license allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
Quickstart
pip install scikit-learn
import scikit-learn
from numpy import array
from scipy import statsVerify before relying
- Whether plotting functions (plot_* and *Display classes) work without explicitly installing Matplotlib as a separate dependency
- Performance characteristics for large datasets or specific algorithm families
- Specific algorithms and model types included in the library beyond what the description excerpt covers
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 5 packagesnumpyscipyjoblibnarwhalsthreadpoolctl |
| Maintenance | Actively maintained 73 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 235,451,583 / month, #171 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/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: CProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonTopic :: Scientific/EngineeringTopic :: Software Development |
Evidence: scikit_learn-1.9.0-cp311-cp311-macosx_10_9_x86_64.whl; scikit_learn-1.9.0-cp311-cp311-macosx_12_0_arm64.whl; scikit_learn-1.9.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; scikit_learn-1.9.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; scikit_learn-1.9.0-cp311-cp311-win_amd64.whl; scikit_learn-1.9.0-cp311-cp311-win_arm64.whl; scikit_learn-1.9.0-cp312-cp312-macosx_10_13_x86_64.whl; scikit_learn-1.9.0-cp312-cp312-macosx_12_0_arm64.whl; scikit_learn-1.9.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; scikit_learn-1.9.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; scikit_learn-1.9.0-cp312-cp312-win_amd64.whl; scikit_learn-1.9.0-cp312-cp312-win_arm64.whl; scikit_learn-1.9.0-cp313-cp313-macosx_10_13_x86_64.whl; scikit_learn-1.9.0-cp313-cp313-macosx_12_0_arm64.whl; scikit_learn-1.9.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; scikit_learn-1.9.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; scikit_learn-1.9.0-cp313-cp313-win_amd64.whl; scikit_learn-1.9.0-cp313-cp313-win_arm64.whl; scikit_learn-1.9.0-cp314-cp314-macosx_10_15_x86_64.whl; scikit_learn-1.9.0-cp314-cp314-macosx_12_0_arm64.whl
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See also scikit-learn-extra · scikit-learn-stubs · scikit-fuzzy · sklearn · hmmlearn · skops · scikit-multilearn · gmr · scikit-survival · kmodes