Sep	OCT	Nov
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2019	2020	2021

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Collection: GDELT Project

TIMESTAMPS

The Wayback Machine - http://web.archive.org/web/20201029095242/https://scikit-learn.org/stable/

● Install ● User Guide ● API ● Examples ● Getting Started ● Tutorial ● What's new ● Glossary ● Development ● FAQ ● Related packages ● Roadmap ● About us ● GitHub ● Other Versions ●

scikit-learn

Machine Learning in Python

Getting Started Release Highlights for 0.23 GitHub ●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: SVM, nearest neighbors, random forest, and more...

Examples

Regression

Predicting a continuous-valued attribute associated with an object. Applications: Drug response, Stock prices. Algorithms: SVR, nearest neighbors, random forest, and more...

Examples

Clustering

Automatic grouping of similar objects into sets. Applications: Customer segmentation, Grouping experiment outcomes Algorithms: k-Means, spectral clustering, mean-shift, and more...

Examples

Dimensionality reduction

Reducing the number of random variables to consider. Applications: Visualization, Increased efficiency Algorithms: k-Means, feature selection, non-negative matrix factorization, and more...

Examples

Model selection

Comparing, validating and choosing parameters and models. Applications: Improved accuracy via parameter tuning Algorithms: grid search, cross validation, metrics, and more...

Examples

Preprocessing

Feature extraction and normalization. Applications: Transforming input data such as text for use with machine learning algorithms. Algorithms: preprocessing, feature extraction, and more...

Examples

News

●On-going development: What's new (Changelog) ●August 2020. scikit-learn 0.23.2 is available for download (Changelog). ●May 2020. scikit-learn 0.23.1 is available for download (Changelog). ●May 2020. scikit-learn 0.23.0 is available for download (Changelog). ●Scikit-learn from 0.23 requires Python 3.6 or greater. ●March 2020. scikit-learn 0.22.2 is available for download (Changelog). ●January 2020. scikit-learn 0.22.1 is available for download (Changelog). ●December 2019. scikit-learn 0.22 is available for download (Changelog and Release Highlights). ●Scikit-learn from 0.21 requires Python 3.5 or greater. ●July 2019. scikit-learn 0.21.3 (Changelog) and 0.20.4 (Changelog) are available for download. ●May 2019. scikit-learn 0.21.0 to 0.21.2 are available for download (Changelog).

Community

●About us: See authors and contributing ●More Machine Learning: Find related projects ●Questions? See FAQ and stackoverflow ●Mailing list: scikit-learn@python.org ●Gitter: gitter.im/scikit-learn ●Communication on all channels should respect PSF's code of conduct.

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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." More testimonials scikit-learn development and maintenance are financially supported by