Gaussian Naive Bayes & K-NN

This lecture covers the topics of Gaussian Naive Bayes and K-nearest neighbors (K-NN). The instructor starts by discussing student feedback and the importance of data-driven improvements. The lecture delves into the probabilistic classification technique of Naive Bayes, explaining the prior probability and generative models. The concept of conditional independence assumption is introduced, along with the implementation of Gaussian Naive Bayes. The instructor also explains the K-NN algorithm, emphasizing the importance of choosing the right value for K and the distance metric. The lecture concludes with insights on the challenges of high-dimensional data and the considerations for hyperparameter tuning.