Regression Models: Performance and Evaluation

This lecture covers the performance evaluation of regression models, focusing on mean absolute error, coefficient of determination, and mean square error. It also delves into the learning and testing errors, over-learning, and under-learning in regression models. The instructor explains the process of training regression models, data slicing methods, and building regression trees using the CART algorithm. The lecture emphasizes the importance of choosing model development criteria, hyperparameters, and validation samples. Practical exercises involve building regression models, resampling dataframes, and creating regression trees.