Lecture
This lecture covers specification testing in classical statistics and machine learning, emphasizing the importance of model evaluation through statistical tests and prediction capability. It discusses the strategy for specification testing, informal tests, statistical hypothesis tests, and goodness of fit. The instructor explains the concept of overfitting in machine learning models and introduces regularization techniques to prevent it. The lecture also delves into prediction tests, market segmentation analysis, and the use of nonlinear specifications. Practical examples and methods for model assessment, selection, and variable selection are presented, along with the concept of bias/variance trade-off in model performance.