ANALYSIS ACCURACY OF CLASSIFICATION FOR MULTI-EVIDENCE DATASET CONTAINING DISCREATE AND CONTINUOUS VALUE USING CATEGORICALNB AND GAUSSIANNB

Machine Learning is one of the emerging fields in computer science, and Bayes’ theorem can be applied to predict class labels precisely and accurately. Naïve Bayes classification, specifically the Categorical Naïve Bayes (CategoricalNB or CNB) classifier, is a simple probabilistic model based on Bayes’ theorem and is highly effective in reducing computational cost, as conditional probabilities can be easily estimated from data. In this paper, we employ both Categorical Naïve Bayes (CNB) and Gaussian Naïve Bayes (GNB) classifiers. The CNB classifier is widely used for binary and multi-class classification problems in machine learning and performs well when the attribute values are categorical or discrete. However, it has certain limitations when handling continuous data. In real-world applications, datasets often contain attributes in mixed formats, including both discrete and continuous values. For continuous attributes, CNB requires discretization, whereas GNB can be directly applied to continuous data by assuming a Gaussian distribution. In this study, we use CNB and GNB to classify a dataset containing both discrete and continuous attributes and compare their classification accuracy. A real-life dataset obtained from the HDFC Bank home loan department was used for experimentation. The dataset consists of 6,000 records with 19 attributes. Experimental results show that the performance of the Gaussian Naïve Bayes classifier is superior to that of the Categorical Naïve Bayes classifier. However, a limitation of GNB is its assumption that continuous attributes follow a normal distribution.