Use of “Odds” in Bayesian Classifiers

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Author(s): Bhushan Kapoor (California State University, Fullerton, USA)and Sinjini Mitra (California State University, Fullerton, USA)
Copyright: 2023
Pages: 14
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch162

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Odds, log odds, and odds ratio concepts can be effectively applied in several machine learning algorithms and model evaluations. The use of these concepts has potential to make the algorithms simple, easy to interpret, and computationally more efficient. However, their implementation among the machine learning professional community has been concentrated mainly in the context of logistic regression. In this article, the authors discuss how odds, odds ratio, and log odds can be used in Bayes' theorem and multinomial naïve Bayes' classifiers. The authors will reformulate Bayes' theorem and multinomial naïve Bayes' classifiers in terms of “odds” and illustrate their applications with examples dealing with “loan application” approval.

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Use of “Odds” in Bayesian Classifiers

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