MRCpy：一个用于 Minimax Risk Classifiers 的库
MRCpy: A Library for Minimax Risk Classifiers
AI总结：
本文提出Python库MRCpy，实现了基于鲁棒风险最小化方法的minimax risk classifiers，提供性能保证、高维高效学习及分布偏移适应能力，并实现与scikit-learn等库的无缝集成。
摘要：
用于监督分类的库促进了机器学习方法的广泛使用。现有的库，如 scikit-learn、caret 和 mlpack，实现了基于经典的经验风险最小化（ERM）方法的技术。我们提出了一个 Python 库 MRCpy，它实现了基于鲁棒风险最小化（RRM）方法的 minimax risk classifiers（MRCs）。该库提供了 MRCs 的多种变体，能够提供性能保证、支持高维环境下的高效学习，并适应分布偏移。MRCpy 遵循面向对象方法并符合 scikit-learn 等流行 Python 库的标准，从而提升了可读性与易用性，并实现了与其他库的无缝集成。源代码在 GPL-3.0 许可下于 https://github.com/MachineLearningBCAM/MRCpy 提供。
英文摘要：
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.
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