Implementing A Naïve Bayes Classifier on Iris Data Using MATLAB, A Classification Method by Using Grid Parameters Optimization

Abstract

Data mining categorization is crucial for predicting outcomes. Naive Bayes Classification (NBC) is a prominent approach used in data mining for classification purposes. It has the ability to anticipate outcomes and is generally more efficient than other techniques of categorization. Several Naive Bayes classification methods exhibit subpar performance when used to classification and regression issues. One of the factors contributing to the success of NBC is the reliance on assumptions of independence among predictors and the initial hyperparameters. Nevertheless, this robust assumption results in a decrease in precision. This work introduces a novel approach to enhance the precision of NBC. The suggested approach employs a grid search to enhance the accuracy of Naïve Bayes classification. The results obtained indicate that the technique utilised achieved a significant degree of accuracy in forecasting compared to the conventional built-in Matlab NBC. Thus, by assuming conditional independence, the accuracy of the NBC may be enhanced.