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.

翻译：监督分类库促进了机器学习方法的广泛应用。现有库（如scikit-learn、caret和mlpack）主要实现基于经典经验风险最小化（ERM）方法的技术。本文提出一个Python库MRCpy，其实现了基于鲁棒风险最小化（RRM）方法的极小化风险分类器（MRCs）。该库提供多种MRC变体，能够提供性能保证、支持高维空间中的高效学习，并适应分布偏移。MRCpy采用面向对象设计，遵循主流Python库（如scikit-learn）的规范，在保证代码可读性与易用性的同时，可与其他库无缝集成。源代码基于GPL-3.0许可证发布于https://github.com/MachineLearningBCAM/MRCpy。