With the explosion of the number of machine learning papers, it becomes increasingly difficult for users and researchers to implement and compare algorithms. Even when authors release their software, it takes time to learn how to use it and how to apply it to one's own purposes. The goal of scikit-learn-contrib is to provide easy-to-install and easy-to-use high-quality machine learning software. With scikit-learn-contrib, users can install a project by
pip install sklearn-contrib-project-name and immediately try it on their data with the usual
transform methods. In addition, projects are compatible with scikit-learn tools such as grid search, pipelines, etc.
If you would like to include your own project in scikit-learn-contrib, take a look at the workflow.
Large-scale linear classification, regression and ranking.
A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines.
Python module to perform under sampling and over sampling with various techniques.
Factorization machines and polynomial networks for classification and regression in Python.
Maintained by Vlad Niculae.
Confidence intervals for scikit-learn forest algorithms.
A high performance implementation of HDBSCAN clustering.
A library of sklearn compatible categorical variable encoders.
Maintained by Will McGinnis
Python implementations of the Boruta all-relevant feature selection method.
Maintained by Daniel Homola
Pandas integration with sklearn.
Maintained by Israel Saeta Pérez
Machine learning with logical rules in Python.
A Python implementation of the stability selection feature selection algorithm.
Maintained by Thomas Huijskens
Metric learning algorithms in Python.