This episode focuses on predictive inference using linear regression methods in high-dimensional settings where the number of predictors (p) often exceeds the number of observations (n). The text primarily explores Lasso regression, explaining its mechanism for variable selection and reducing overfitting by penalizing coefficient magnitudes. It also compares Lasso to other penalized regression techniques like Ridge, Elastic Net, and Lava, discussing their suitability for different data structures such as sparse, dense, or sparse+dense coefficient vectors, and emphasizes the importance of cross-validation for selecting optimal tuning parameters.
Disclosure
