Implementation of Naive Bayes Algorithm in MATLAB

The Naive Bayes algorithm is a simple probability-based classification algorithm grounded in Bayes' theorem, particularly well-suited for handling high-dimensional data. Implementing this algorithm in MATLAB primarily involves three critical steps: probability calculation, prior estimation, and classification prediction. Unlike directly calling built-in toolbox functions, manual implementation provides deeper insight into the algorithm's core mechanics. Algorithm implementation typically begins with data preprocessing, requiring separation of sample features and class labels. For discrete features, compute conditional probabilities for each feature value across different classes; for continuous features, Gaussian distribution is assumed, requiring calculation of mean and variance parameters. The training phase focuses on statistical frequency counts per class as prior probabilities and constructing conditional probability tables. During the prediction phase, the algorithm computes joint probabilities for each class regarding the test sample, selecting the class with the highest probability as the prediction outcome. To prevent numerical underflow issues, practical implementations often employ logarithmic probability summation instead of direct probability multiplication. When encapsulating functions, special attention must be paid to input data dimensionality and type validation to ensure feature consistency. This fundamental implementation includes details like probability smoothing techniques. Compared to using the fitcnb function directly, custom implementation offers greater flexibility to adjust probability calculation methods based on data characteristics. For specific applications like text classification, the algorithm can be extended to polynomial or Bernoulli Naive Bayes variants through appropriate modifications to probability estimation functions.