Comparison Study of Generative and Discriminative Models for Classification of Classifiers

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.