【359】scikit learn 官方帮助文档

官方网站链接

• sklearn.neighbors.KNeighborsClassifier
• sklearn.tree.DecisionTreeClassifier
• sklearn.naive_bayes.MultinomialNB
• sklearn.linear_model.LogisticRegression
• sklearn.svm.SVC

 

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• Home
• Installation
• Documentation

  • Scikit-learn 0.20.2 (stable)
  • Tutorials
  • User guide
  • API
  • Glossary
  • FAQ
  • Contributing
  • Roadmap

  • Development version
  • All available versions
  • PDF documentation
• Examples

Documentation of scikit-learn 0.20.2¶

Quick Start

A very short introduction into machine learning problems and how to solve them using scikit-learn. Presents basic concepts and conventions.

User Guide

The main documentation. This contains an in-depth description of all algorithms and how to apply them.

Other Versions

• Development version
• All available versions
• PDF documentation

Tutorials

Useful tutorials for developing a feel for some of scikit-learn's applications in the machine learning field.

Glossary

The definitive description of key concepts and API elements for using scikit-learn and developing compatible tools.

API

The exact API of all functions and classes, as given by the docstrings. The API documents expected types and allowed features for all functions, and all parameters available for the algorithms.

Development

Information on how to contribute. This also contains useful information for advanced users, for example how to build their own estimators.

FAQ

Frequently asked questions about the project and contributing.

Additional Resources

Talks given, slide-sets and other information relevant to scikit-learn.

Flow Chart

A graphical overview of basic areas of machine learning, and guidance which kind of algorithms to use in a given situation.

Related packages

Other machine learning packages for Python and related projects. Also algorithms that are slightly out of scope or not well established enough for scikit-learn.

Roadmap

Roadmap of the project.