General Understanding of The Procedures for Applying The Naïve Bayes Classifier to Classify Topics

Abstract

The Naive Bayes classifier is one of the first and most famous examples of a supervised machine learning algorithm based on the Bayes theorem. Naive Bayes is mainly used for text classification and based on the principles of probability, with certain assumptions that make it computationally efficient. The Naive Bayes classifier can be quite efficient, but assumptions like the conditional independence of features, which are often untrue, can lead to reduced performance in real-world applications. The purpose of this paper is to introduce the mechanisms behind the Naive Bayes classifier and to demonstrate the implementation of Naive Bayes in text classification. If we apply Naive Bayes in spam email filtering, the model calculates conditional, prior probabilities to predict if an email is spam or not. It talks about the use of Maximum Likelihood Estimation (MLE) to compute the probabilities used in text classification along with some information on the use of confusion matrices to evaluate the performance of classifiers. These results indicate the importance of data preprocessing and addressing feature dependence in real-life applications of Naive Bayes and suggest meaningful avenues for improving its performance.