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The Naive Bayes (NB) algorithm, a widely adopted probabilistic classification technique, holds significant importance across various domains such as natural language processing, spam detection, and sentiment analysis. This study thoroughly investigates the foundational principles of NB, Bayesian inference, and its practical implementations. Emphasizing its simplicity and efficiency, NB relies on the "naive" assumption of feature independence as its core principle. The study examines the implications of this assumption on model performance and offers strategies for addressing real-world deviations. Comparisons are drawn with four research papers that delve into different facets of Naive Bayes. The first paper, "Hidden Naive Bayes," explores methods for uncovering concealed dependencies within data and introduces a novel algorithm for this purpose. The second paper, "Learning the Naive Bayes Classifier with Optimization Models," investigates optimization techniques to enhance the performance of the Naive Bayes classifier. In contrast, the third paper, "Naive Bayes for Regression," explores the utilization of Naive Bayes in regression analysis. Lastly, the fourth paper, "Naive Bayes Classifiers," discusses various variants of NB tailored for different data types and presents comparative analyses across diverse scenarios.
Kumar et al. (Fri,) studied this question.