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.

Just pip install featureforge.

Documentation is available at http://feature-forge.readthedocs.org/en/latest/

Feature Forge is © 2014 Machinalis (http://www.machinalis.com/). Its primary authors are:

• Javier Mansilla <jmansilla@machinalis.com> (jmansilla at github)
• Daniel Moisset <dmoisset@machinalis.com> (dmoisset at github)
• Rafael Carrascosa <rcarrascosa@machinalis.com> (rafacarrascosa at github)

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

0.1: Initial release