In classification of classifier analysis, researchers have been worried about the classifier of existing generative and discriminative models in practice for analyzing attributes data. This makes it necessary to give an in-depth, systematic, interrelated, interconnected, and classification of classifier of generative and discriminative models. Generative models of Logistic and Multinomial Logistic regression models and discriminative models of Linear Discriminant Analysis (LDA) (for attribute P=1 and P>1), Quadratic Discriminant Analysis (QDA) and Naïve Bayes were thoroughly dealt with analytically and mathematically. A step-by-step empirical analysis of the mentioned models were carried-out via chemical analysis of wines grown in a region in Italy that was derived from three different cultivars (The three types of wines that constituted the three different cultivars or three classifiers). Naïve Bayes Classifier set the pace via leading a-prior probabilities.

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.