scikit-learn: machine learning in Python — scikit-learn 0.17.dev0 documentation

Classification

Identifying to which category an object belongs to.

Applications: Spam detection, Image recognition.
Algorithms:
SVM, nearest neighbors, random forest, ...

Examples

Regression

Predicting a continuous-valued attribute associated with an object.

Applications: Drug response, Stock prices.
Algorithms:
SVR, ridge regression, Lasso, ...

Examples

Clustering

Automatic grouping of similar objects into sets.

Applications: Customer segmentation, Grouping experiment outcomes
Algorithms:
k-Means, spectral clustering, mean-shift, ...

Examples

Dimensionality reduction

Reducing the number of random variables to consider.

Applications: Visualization, Increased efficiency
Algorithms:
PCA, feature selection, non-negative matrix factorization.

Examples

Model selection

Comparing, validating and choosing parameters and models.

Goal: Improved accuracy via parameter tuning
Modules:
grid search, cross validation, metrics.

Examples

Preprocessing

Feature extraction and normalization.

Application: Transforming input data such as text for use with machine learning algorithms.
Modules:
preprocessing, feature extraction.

Examples

News

On-going development: What's new (changelog)
March 2015. scikit-learn 0.16.0 is available for download (Changelog).
July 2014. scikit-learn 0.15.0 is available for download (Changelog).
July 14-20th, 2014: international sprint. During this week-long sprint, we gathered 18 of the core contributors in Paris. We want to thank our sponsors: Paris-Saclay Center for Data Science & Digicosme and our hosts La Paillasse, Criteo, Inria, and tinyclues.
August 2013. scikit-learn 0.14 is available for download (Changelog).