MRCpy: A Library for Minimax Risk Classifiers
This provides a tool for researchers and practitioners needing robust classification with theoretical guarantees, but it is incremental as it builds on existing robust risk minimization methods.
The authors tackled the lack of libraries for robust classification by introducing MRCpy, a Python library that implements minimax risk classifiers based on robust risk minimization, offering performance guarantees and adaptability to distribution shifts.
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.