scikit-learn: machine learning in Python

Easy-to-use and general-purpose machine learning in Python

scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit scientific Python world (numpy, scipy, matplotlib). It aims to provide simple and efficient solutions to learning problems, accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering.

Supervised learning Support vector machines, linear models, naives Bayes, Gaussian process...

Unsupervised learning Clustering, Gaussian mixture models, manifold learning, matrix factorization, covariance...

And much more Model selection, datasets, feature extraction... See below.

License: Open source, commercially usable: BSD license (3 clause)

Documentation for scikit-learn version 0.11-git. For other versions and printable format, see Documentation resources.

User Guide

• 1. Installing scikit-learn
  • 1.1. Installing an official release
  • 1.2. Third party distributions of scikit-learn
  • 1.3. Bleeding Edge
  • 1.4. Testing
• 2. Tutorials: From the bottom up with scikit-learn
  • 2.1. An Introduction to machine learning with scikit-learn
  • 2.2. Scikit-learn tutorial: statistical-learning for sientific data processing
  • 2.3. Using scikit-learn with Astronomical Data
  • 2.4. Machine Learning for Text Analytics with scikit-learn
• 3. Supervised learning
  • 3.1. Generalized Linear Models
  • 3.2. Support Vector Machines
  • 3.3. Stochastic Gradient Descent
  • 3.4. Nearest Neighbors
  • 3.5. Gaussian Processes
  • 3.6. Partial Least Squares
  • 3.7. Naive Bayes
  • 3.8. Decision Trees
  • 3.9. Ensemble methods
  • 3.10. Multiclass and multilabel algorithms
  • 3.11. Feature selection
  • 3.12. Semi-Supervised
  • 3.13. Linear and Quadratic Discriminant Analysis
• 4. Unsupervised learning
  • 4.1. Gaussian mixture models
  • 4.2. Manifold learning
  • 4.3. Clustering
  • 4.4. Decomposing signals in components (matrix factorization problems)
  • 4.5. Covariance estimation
  • 4.6. Novelty and Outlier Detection
  • 4.7. Hidden Markov Models
• 5. Model Selection
  • 5.1. Cross-Validation
  • 5.2. Grid Search
• 6. Dataset transformations
  • 6.1. Preprocessing data
  • 6.2. Feature extraction
  • 6.3. Kernel Approximation
• 7. Dataset loading utilities
  • 7.1. General dataset API
  • 7.2. Toy datasets
  • 7.3. Sample images
  • 7.4. Sample generators
  • 7.5. Datasets in svmlight / libsvm format
  • 7.6. The Olivetti faces dataset
  • 7.7. The 20 newsgroups text dataset
  • 7.8. Downloading datasets from the mldata.org repository
  • 7.9. The Labeled Faces in the Wild face recognition dataset
• 8. Reference
  • 8.1. sklearn.cluster: Clustering
  • 8.2. sklearn.covariance: Covariance Estimators
  • 8.3. sklearn.cross_validation: Cross Validation
  • 8.4. sklearn.datasets: Datasets
  • 8.5. sklearn.decomposition: Matrix Decomposition
  • 8.6. sklearn.ensemble: Ensemble Methods
  • 8.7. sklearn.feature_extraction: Feature Extraction
  • 8.8. sklearn.feature_selection: Feature Selection
  • 8.9. sklearn.gaussian_process: Gaussian Processes
  • 8.10. sklearn.grid_search: Grid Search
  • 8.11. sklearn.hmm: Hidden Markov Models
  • 8.12. sklearn.kernel_approximation Kernel Approximation
  • 8.13. sklearn.semi_supervised Semi-Supervised Learning
  • 8.14. sklearn.lda: Linear Discriminant Analysis
  • 8.15. sklearn.linear_model: Generalized Linear Models
  • 8.16. sklearn.manifold: Manifold Learning
  • 8.17. sklearn.metrics: Metrics
  • 8.18. sklearn.mixture: Gaussian Mixture Models
  • 8.19. sklearn.multiclass: Multiclass and multilabel classification
  • 8.20. sklearn.naive_bayes: Naive Bayes
  • 8.21. sklearn.neighbors: Nearest Neighbors
  • 8.22. sklearn.pls: Partial Least Squares
  • 8.23. sklearn.pipeline: Pipeline
  • 8.24. sklearn.preprocessing: Preprocessing and Normalization
  • 8.25. sklearn.qda: Quadratic Discriminant Analysis
  • 8.26. sklearn.svm: Support Vector Machines
  • 8.27. sklearn.tree: Decision Trees
  • 8.28. sklearn.utils: Utilities

Example Gallery

• Examples
  • General examples
  • Examples based on real world datasets
  • Clustering
  • Covariance estimation
  • Decomposition
  • Ensemble methods
  • Gaussian Process for Machine Learning
  • Generalized Linear Models
  • Manifold learning
  • Gaussian Mixture Models
  • Nearest Neighbors
  • Semi Supervised Classification
  • Support Vector Machines
  • Decision Trees

Development

• Contributing
  • Submitting a bug report
  • Retrieving the latest code
  • Contributing code
  • Other ways to contribute
  • Coding guidelines
  • APIs of scikit-learn objects
• How to optimize for speed
  • Python, Cython or C/C++?
  • Profiling Python code
  • Performance tips for the Cython developer
  • Profiling compiled extensions
  • Multi-core parallelism using joblib.Parallel
  • A sample algorithmic trick: warm restarts for cross validation
• Utilities for Developers
  • Validation Tools
  • Efficient Linear Algebra & Array Operations
  • Efficient Routines for Sparse Matrices
  • Graph Routines
  • Backports
  • Testing Functions
  • Helper Functions
  • Hash Functions
  • Warnings and Exceptions
• Developers’ Tips for Debugging
  • Memory errors: debugging Cython with valgrind
• About us
  • History
  • People
  • Citing scikit-learn
  • Funding