Related papers: MRCpy: A Library for Minimax Risk Classifiers

MRCpy: A Library for Minimax Risk Classifiers

URL: http://arxiv.org/abs/2108.01952v4
Date: Wed, 29 May 2024 13:51:15 GMT
Title: MRCpy: A Library for Minimax Risk Classifiers
Authors: Kartheek Bondugula, Verónica Álvarez, José I. Segovia-Martín, Aritz Pérez, Santiago Mazuelas,
Abstract summary: Python library, MRCpy, implements minimax risk classifiers (MRCs) based on the robust risk minimization (RRM) approach. MRCpy follows the standards of popular Python libraries, such as scikit-learn, facilitating readability and easy usage together with a seamless integration with other libraries.
Score: 10.380882297891272
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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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