A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
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:
Just pip install featureforge.
Documentation is available at http://feature-forge.readthedocs.org/en/latest/
Feature Forge is copyright 2014 Machinalis (http://www.machinalis.com/). Its primary authors are:
Any contributions or suggestions are welcome, the official channel for this is submitting github pull requests or issues.
0.1.7: - StatsManager api change (order of arguments swapped) - For experimentation, enabled a way of booking experiments forever.
0.1.6: - Bug fixes related to sparse matrices. - Small documentation improvements. - Reduced default logging verbosity.
0.1.5: - Using sparse numpy matrices by default.
0.1.4: - Discarded the need of using forked version of Schema library.
0.1.3: - Added support for running and generating stats for experiments
0.1.2: - Fixing installer dependencies
0.1.1: - Added support for python 3 - Added support for bag-of-words features
0.1: - Initial release
No open issues yet, or sync has not completed.