  Libraries for supervised classification have enabled the wide-spread usage of
machine learning methods. Existing libraries, such as scikit-learn, caret, and
mlpack, implement techniques based on the classical empirical risk minimization
(ERM) approach. We present a Python library, MRCpy, that implements minimax
risk classifiers (MRCs) based on the robust risk minimization (RRM) approach.
The library offers multiple variants of MRCs that can provide performance
guarantees, enable efficient learning in high dimensions, and adapt to
distribution shifts. MRCpy follows an object-oriented approach and adheres to
the standards of popular Python libraries, such as scikit-learn, facilitating
readability and easy usage together with a seamless integration with other
libraries. The source code is available under the GPL-3.0 license at
https://github.com/MachineLearningBCAM/MRCpy.
