MRCpy: A Library for Minimax Risk Classifiers
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| _version_ | 1866914815024300032 |
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| author | Bondugula, Kartheek Álvarez, Verónica Segovia-Martín, José I. Pérez, Aritz Mazuelas, Santiago |
| author_facet | Bondugula, Kartheek Álvarez, Verónica Segovia-Martín, José I. Pérez, Aritz Mazuelas, Santiago |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2108_01952 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | MRCpy: A Library for Minimax Risk Classifiers Bondugula, Kartheek Álvarez, Verónica Segovia-Martín, José I. Pérez, Aritz Mazuelas, Santiago Machine Learning 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. |
| title | MRCpy: A Library for Minimax Risk Classifiers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2108.01952 |