A new model for iris classification based on Naïve Bayes grid parameters optimization

Data mining classification plays an important role in the prediction of outcomes. One of the outstanding classifications methods in data mining is Naive Bayes Classification (NBC). It is capable of envisaging results and mostly effective than other classification methods. Many Naive Bayes classification method provide low performance in classification and regression problems  Ones of the facts behinds the performances of the NBC is dues to the assumptions of contingent on independence amidst predictors and the initials hyper parameters. However, this strong assumption leads to loss of accuracy. In this study, a new method for boosting the accuracy of NBC was proposed. The proposed new technique uses a grid search to give better accuracy Naive Bayes classification.