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A library to build and test machine learning features

Project description

This library provides a set of tools that can be useful in many machine learning applications (classification, clustering, regression, etc.), and particularly helpful if you use scikit-learn (although this can work if you have a different algorithm).

Most machine learning problems involve an step of feature definition and preprocessing. Feature Forge helps you with:

  • Defining and documenting features

  • Testing your features against specified cases and against randomly generated cases (stress-testing). This helps you making your application more robust against invalid/misformatted input data. This also helps you checking that low-relevance results when doing feature analysis is actually because the feature is bad, and not because there’s a slight bug in your feature code.

  • Evaluating your features on a data set, producing a feature evaluation matrix. The evaluator has a robust mode that allows you some tolerance both for invalid data and buggy features.

  • Experimentation: running, registering, classifying and reproducing experiments for determining best settings for your problems.


Just pip install featureforge.


Documentation is available at

Contact information

Feature Forge is © 2014 Machinalis ( Its primary authors are:

Any contributions or suggestions are welcome, the official channel for this is submitting github pull requests or issues.


  • Bug fixes related to sparse matrices.

  • Small documentation improvements.

  • Reduced default logging verbosity.

  • Using sparse numpy matrices by default.

  • Discarded the need of using forked version of Schema library.

  • Added support for running and generating stats for experiments

  • Fixing installer dependencies

  • Added support for python 3

  • Added support for bag-of-words features

  • Initial release

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