Naive Bayes Classifier

This lecture covers the Naive Bayes classifier, starting with the independence assumption for feature attributes. It then delves into the conditional probabilities and modeling the distribution for classification. The lecture further explores the application of the classifier in scenarios like document classification and medical diagnosis, emphasizing the simplicity and effectiveness of the method. Additionally, it discusses the extension of the classifier to handle continuous feature values using Gaussian distributions. The presentation concludes with the algorithm for Gaussian feature data classification.