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Existing libraries for supervised classification implement techniques thatare based on empirical risk minimization and utilize surrogate losses. Wepresent MRCpy library that implements minimax risk classifiers (MRCs) that arebased on robust risk minimization and can utilize 0-1-loss. Such techniquesgive rise to a manifold of classification methods that can provide tight boundson the expected loss. MRCpy provides a unified interface for different variantsof MRCs and follows the standards of popular Python libraries. The presentedlibrary also provides implementation for popular techniques that can be seen asMRCs such as L1-regularized logistic regression, zero-one adversarial, andmaximum entropy machines. In addition, MRCpy implements recent feature mappingssuch as Fourier, ReLU, and threshold features. The library is designed with anobject-oriented approach that facilitates collaborators and users.

MRCpy: A Library for Minimax Risk Classifiers