Ensemble Methods: Random Forests

This lecture covers ensemble methods, focusing on random forests and Gaussian Naive Bayes. It explains the concept of ensemble learning, the construction of decision trees, and the use of bagging, boosting, and stacking. The instructor discusses the random forest algorithm, which combines multiple decision trees to improve prediction accuracy. Additionally, the lecture delves into Gaussian Naive Bayes, a probabilistic classifier based on Bayes' theorem and the assumption of feature independence. The instructor demonstrates how Gaussian Naive Bayes estimates the conditional Gaussian distribution for each feature using sample data.