| CARVIEW |
- Simple and efficient tools for predictive data analysis
- Accessible to everybody, and reusable in various contexts
- Built on NumPy, SciPy, and matplotlib
- Open source, commercially usable - BSD license
Classification
Identifying which category an object belongs to.
Applications: Spam detection, image recognition. Algorithms: Gradient boosting, nearest neighbors, random forest, logistic regression, and more...
Regression
Predicting a continuous-valued attribute associated with an object.
Applications: Drug response, stock prices. Algorithms: Gradient boosting, nearest neighbors, random forest, ridge, and more...
Clustering
Automatic grouping of similar objects into sets.
Applications: Customer segmentation, grouping experiment outcomes. Algorithms: k-Means, HDBSCAN, hierarchical clustering, and more...
Dimensionality reduction
Reducing the number of random variables to consider.
Applications: Visualization, increased efficiency. Algorithms: PCA, feature selection, non-negative matrix factorization, and more...
Model selection
Comparing, validating and choosing parameters and models.
Applications: Improved accuracy via parameter tuning. Algorithms: Grid search, cross validation, metrics, and more...
Preprocessing
Feature extraction and normalization.
Applications: Transforming input data such as text for use with machine learning algorithms. Algorithms: Preprocessing, feature extraction, and more...
News
- On-going development: scikit-learn 1.9 (Changelog).
- December 2025. scikit-learn 1.8.0 is available for download (Changelog).
- September 2025. scikit-learn 1.7.2 is available for download (Changelog).
- July 2025. scikit-learn 1.7.1 is available for download (Changelog).
- June 2025. scikit-learn 1.7.0 is available for download (Changelog).
- January 2025. scikit-learn 1.6.1 is available for download (Changelog).
- December 2024. scikit-learn 1.6.0 is available for download (Changelog).
- All releases: What's new (Changelog).
Community
- About us: See people and contributing
- More Machine Learning: Find related projects
- Questions? See FAQ, Support, Discussions, and Stack Overflow
- Subscribe to the mailing list
- Blog: blog.scikit-learn.org
- Logos & Branding: logos and branding
- Calendar: calendar
- LinkedIn: linkedin/scikit-learn
- Bluesky: bluesky/scikit-learn.org
- Mastodon: @sklearn
- YouTube: youtube.com/scikit-learn
- Facebook: @scikitlearnofficial
- Instagram: @scikitlearnofficial
- TikTok: @scikit.learn
- Discord: @scikit-learn
- Communication on all channels should respect our code of conduct.
Who uses scikit-learn?
"We use scikit-learn to support leading-edge basic research [...]"
"I think it's the most well-designed ML package I've seen so far."
"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]"
"The great benefit of scikit-learn is its fast learning curve [...]"
"It allows us to do AWesome stuff we would not otherwise accomplish."
"scikit-learn makes doing advanced analysis in Python accessible to anyone."
