Open AccessMultinomial Naïve Bayes Classifier: Bayesian versus Nonparametric Classifier Approach
Author(s) -
Rasaki Olawale Olanrewaju,
Sodiq Adejare Olanrewaju,
Lukman Abiodun Nafiu
Publication year - 2022
Publication title -
european journal of statistics
Language(s) - English
Resource type - Journals
ISSN - 2806-0954
DOI - 10.28924/ada/stat.2.8
Subject(s) - multinomial logistic regression , nonparametric statistics , bayes' theorem , statistics , categorical variable , mathematics , nonparametric regression , naive bayes classifier , logistic regression , artificial intelligence , bayesian probability , computer science , support vector machine
This paper proposes a Naïve Bayes Classifier for Bayesian and nonparametric methods of analyzing multinomial regression. The Naïve Bayes classifier adopted Bayes’ rule for solving the posterior of the multinomial regression via its link function known as Logit link. The nonparametric adopted Gaussian, bi-weight kernels, Silverman’s rule of thumb bandwidth selector, and adjusted bandwidth as kernel density estimation. Three categorical responses of information on 78 people using one of three diets (Diet A, B, and C) that consist of scaled variables: age (in years), height (in cm), weight (in kg) before the diet (that is, pre-weight), weight (in kg) gained after 6 weeks of diet were subjected to the classifier multinomial regression of Naïve Bayes and nonparametric. The Gaussian and bi-weight kernel density estimation produced the minimum bandwidths across the three categorical responses for the four influencers. The Naïve Bayes classifier and nonparametric kernel density estimation for the multinomial regression produced the same prior probabilities of 0.3077, 0.3462, and 0.3462; and A prior probabilities of 0.3077, 0.3462, and 0.3462 for Diet A, Diet B, and Diet C at different smoothing bandwidths.