Feature Selection - Linear Regression, Ridge Regression, and Lasso - L19

Learn feature selection techniques in machine learning with a focus on linear regression methods including Ridge Regression, Lasso, and Orthogonal Matching Pursuit (OMP). Discover why Lasso regression produces sparse feature sets and understand the critical issues involved in interpreting sparse features, including the impact of different measurement units. Explore normalization techniques and the challenges posed by co-linearity in datasets. Examine why large coefficients don't necessarily indicate feature importance, as they may simply reflect correlation rather than causation. Understand the complexities involved in feature selection decisions and when these methods work effectively despite potentially complex solutions.