A MATLAB Implementation of the Naive Bayes Algorithm

This article introduces a MATLAB implementation of the Naive Bayes algorithm, a simple yet effective classifier for solving classification problems. The algorithm relies on Bayes' theorem and the feature independence assumption to determine the class of given data points. In this MATLAB implementation, we will build a classifier to categorize data points into different classes. We begin by explaining Bayes' theorem and the feature independence assumption, then demonstrate how these concepts are applied to classify data points. The implementation involves calculating prior probabilities from training data and computing likelihoods using probability density functions (for continuous features) or frequency counts (for discrete features). Key MATLAB functions like `fitcnb` for model training and `predict` for classification will be discussed with code snippets. Furthermore, we cover optimization techniques to enhance algorithm performance. Cross-validation methods (e.g., using `cvpartition`) can evaluate classifier accuracy, while smoothing techniques (like Laplace smoothing) handle zero-probability issues. For multiclass classification, we implement multinomial distribution approaches with examples showing probability normalization across classes. Finally, we explore practical applications where Naive Bayes algorithms excel, including spam filtering (using bag-of-words feature extraction), text classification, and sentiment analysis. The MATLAB implementation will include dataset preprocessing steps such as term-frequency vectorization using built-in functions like `bagOfWords`. Through this article, readers will learn to implement Naive Bayes in MATLAB with practical code examples and understand its real-world applications through algorithmic enhancements and performance evaluation techniques.