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Statistics > Machine Learning

arXiv:2108.01952 (stat)
[Submitted on 4 Aug 2021 (v1), last revised 29 May 2024 (this version, v4)]

Title:MRCpy: A Library for Minimax Risk Classifiers

Authors:Kartheek Bondugula, Verónica Álvarez, José I. Segovia-Martín, Aritz Pérez, Santiago Mazuelas
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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 this https URL.
Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as: arXiv:2108.01952 [stat.ML]
  (or arXiv:2108.01952v4 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.2108.01952

Submission history

From: Kartheek Bondugula [view email]
[v1] Wed, 4 Aug 2021 10:31:20 UTC (11 KB)
[v2] Mon, 24 Apr 2023 15:02:59 UTC (11 KB)
[v3] Tue, 16 May 2023 11:21:11 UTC (11 KB)
[v4] Wed, 29 May 2024 13:51:15 UTC (669 KB)
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