About

What is scikit-learn?

scikit-learn is a popular machine learning package for Python that has simple and efficient tools for predictive data analysis. Topics include classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. It is built upon NumPy, SciPy, and matplotlib and is open-sourced under the BSD License. It is part of the scientific computation ecosystem and useful for both individual and commercial use.

New to scikit-learn?

There are various resources including books, tutorials/workshops, etc. for those looking to learn how to use scikit-learn.

A popular introductory tutorial is:

SciPy 2018 Conference Tutorial:

• Machine Learning with scikit-learn - Part 1
• Machine Learning with scikit-learn - Part 2

A popular introductory book is:

Introduction to Machine Learning with Python, by Andreas C. Müller and Sarah Guido.

scikit-learn Tag usage

When posting questions about scikit-learn, please take the following into consideration:

• When tagging questions with the scikit-learn tag, users should not use the tag sklearn, despite semantic similarity, as the latter is marked as a synonym and will automatically be retagged.

• Explicit programming related questions are more suitable for Stack Overflow and should not be posted on Stack Exchange Data Science.

• Questions should include sufficient details and clarity to be able to provide support for the problem at hand. This includes linking to underlying data used, providing code used for the model's construction, highlighting relevant outputs, etc.

External Resources

scikit-learn: Documentation page

scikit-learn: GitHub page

Important links

• HTML documentation (development version): http://scikit-learn.org/dev/
• Download releases: http://sourceforge.net/projects/scikit-learn/files/
• Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
• Mailing list: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general