Existing libraries for supervised classification implement techniques that are based on empirical risk minimization and utilize surrogate losses. We present MRCpy library that implements minimax risk classifiers (MRCs) that are based on robust risk minimization and can utilize 0-1-loss. Such techniques give rise to a manifold of classification methods that can provide tight bounds on the expected loss. MRCpy provides a unified interface for different variants of MRCs and follows the standards of popular Python libraries. The presented library also provides implementation for popular techniques that can be seen as MRCs such as L1-regularized logistic regression, zero-one adversarial, and maximum entropy machines. In addition, MRCpy implements recent feature mappings such as Fourier, ReLU, and threshold features. The library is designed with an object-oriented approach that facilitates collaborators and users.

翻译：现有的监督分类库实现了基于经验风险最小化并利用替代损失的技术。我们提出了MRCpy库，该库实现了基于鲁棒风险最小化且能使用0-1损失的最小最大风险分类器(MRCs)。这类技术衍生出一系列分类方法，可为期望损失提供紧界。MRCpy为不同变体的MRCs提供了统一接口，并遵循主流Python库的标准。该库还实现了可视为MRCs的流行技术，如L1正则化逻辑回归、零一对抗模型和最大熵机。此外，MRCpy实现了近期提出的特征映射，包括傅里叶、ReLU和阈值特征。该库采用面向对象方法设计，便于合作者及用户使用。