Naive Bayes Classification Implementation in MATLAB

The Naive Bayes classifier represents a fundamental classification algorithm extensively applied in pattern recognition and machine learning domains. This implementation utilizes MATLAB's computational capabilities to create a probabilistic model based on Bayes' theorem with strong feature independence assumptions. The package includes properly structured training and test datasets, enabling direct execution without additional configuration. Key implementation aspects include probability estimation using frequency counting, Laplace smoothing for handling zero-frequency cases, and logarithmic computations for numerical stability during classification. The readme.txt document comprehensively outlines the required data format - typically containing feature vectors and corresponding labels - along with step-by-step usage guidelines. By leveraging this Naive Bayes classifier implementation, users can efficiently address classification challenges while achieving statistically robust results through probability-based decision boundaries.