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Article

A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification

1
TOELT LLC, Machine Learning Research and Development, Birchlenstr. 25, 8600 Dübendorf, Switzerland
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PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
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IDSIA—Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Via la Santa 1, 6962 Lugano, Switzerland
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Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editor: Frank Werner
Algorithms 2021, 14(11), 301; https://doi.org/10.3390/a14110301
Received: 29 September 2021 / Revised: 12 October 2021 / Accepted: 15 October 2021 / Published: 20 October 2021
This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features regardless of the model used. This limit, namely, the Bayes error, is completely independent of any model used and describes an intrinsic property of the dataset. The ILD algorithm thus provides important information regarding the prediction limits of any binary classification algorithm when applied to the considered dataset. In this paper, the algorithm is described in detail, its entire mathematical framework is presented and the pseudocode is given to facilitate its implementation. Finally, an example with a real dataset is given. View Full-Text
Keywords: machine learning; intrinsic limits; ROC curve; binary classification; area under the curve; Naïve Bayes classifier machine learning; intrinsic limits; ROC curve; binary classification; area under the curve; Naïve Bayes classifier
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MDPI and ACS Style

Michelucci, U.; Sperti, M.; Piga, D.; Venturini, F.; Deriu, M.A. A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification. Algorithms 2021, 14, 301. https://doi.org/10.3390/a14110301

AMA Style

Michelucci U, Sperti M, Piga D, Venturini F, Deriu MA. A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification. Algorithms. 2021; 14(11):301. https://doi.org/10.3390/a14110301

Chicago/Turabian Style

Michelucci, Umberto, Michela Sperti, Dario Piga, Francesca Venturini, and Marco A. Deriu. 2021. "A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification" Algorithms 14, no. 11: 301. https://doi.org/10.3390/a14110301

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