Implementing Naive Bayes Classification with the IRIS Dataset

Although MATLAB offers built-in Naive Bayes functions, implementing the algorithm manually provides better insight into its underlying mechanics. Below is a custom Naive Bayes classification program using the IRIS dataset:

The implementation begins by loading the IRIS dataset and performing random splitting into training and testing sets (typically 70-30 or 80-20 ratios). For each class in the training data, we calculate feature means and standard deviations. These parameters feed into Gaussian probability density functions for likelihood estimation. During classification, the algorithm computes posterior probabilities for each test sample using Bayes' theorem with the "naive" assumption of feature independence. Predictions are compared against actual labels to evaluate performance.

Key implementation details include: using Gaussian NB for continuous features, handling class priors based on training distribution, and applying log probabilities to avoid numerical underflow. The final evaluation metrics include accuracy and recall scores, calculated through confusion matrix analysis. This hands-on approach not only solidifies understanding of Naive Bayes but also provides a template for comparing other classification algorithms like SVM or decision trees in terms of efficiency and applicability.