Variable Selection Methods: Subset vs. Container Approaches

This lecture covers the concept of variable selection in machine learning, focusing on subset selection and container methods. Subset selection involves determining the best subset of variables for a learning algorithm, while container methods use algorithms to select variables. The instructor explains the naive method, which exhaustively tries all combinations of variables, and non-exhaustive methods like forward and backward search. The lecture also introduces floating search, a greedy procedure for selecting variables. Examples and workout scenarios are provided to illustrate the different selection approaches.