#66th of 7 in ML frameworks and libraries
scikit-learn
Python library of classical machine learning algorithms built on NumPy and SciPy
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- Stars
- 67.6k
- License
- BSD-3-Clause
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- Python
Checked on GitHub.
Overview
scikit-learn is a Python module for machine learning built on SciPy, started in 2007 as a Google Summer of Code project. It installs with pip or conda and requires Python 3.12 or newer, NumPy, SciPy, Narwhals, joblib and threadpoolctl. Plotting helpers need Matplotlib. The README does not list the algorithms; those are documented on the website.
Who it is for: Python developers and data scientists building classical ML models
Strengths
- Installs with one pip or conda command; six required dependencies, all listed with minimum versions
- BSD-3-Clause license; test suite runs with pytest sklearn
- Volunteer community with institutional support; benchmarked with asv
- Test randomness is controllable through the SKLEARN_SEED environment variable
Weaknesses
- README does not mention GPU support
- Requires Python 3.12 or newer, which excludes older environments
- Plotting needs Matplotlib, installed separately
- README gives no algorithm list; readers must consult the website
What it needs
- Needs Python >= 3.12, NumPy >= 1.26.0, SciPy >= 1.11.4, Narwhals >= 2.0.1, joblib >= 1.4.0, threadpoolctl >= 3.5.0
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