AI Quant Trader Series Day 7: Lasso Regression as a Feature Selection Tool

The seventh installment of the godzilla.dev AI Quant Trader Series focuses on Lasso regression and its application in financial modeling. Unlike ordinary least squares, Lasso adds a penalty term that forces less important feature coefficients to zero, effectively performing automatic feature selection. The tutorial walks through implementing Lasso in Python using scikit-learn, including how to tune the alpha hyperparameter via cross-validation. The guide also highlights key limitations of Lasso, such as poor performance with highly correlated features and sensitivity to outliers. Suggested remedies include Elastic Net regularization, PCA combined with Lasso, and using Lasso as a preprocessing step before non-linear models.