godzilla.dev — AI Quant Trader Series — Day 7

Short summary

Day 7 of an AI quant trader series explores Lasso regression—a regularized linear model that automatically performs feature selection and prevents coefficient instability when features are correlated. Covers Python implementation with sklearn, alpha tuning via cross-validation, and limitations including poor performance on non-linear interactions and outliers.

• •Lasso regression adds L1 penalty to force automatic feature selection and model simplicity
• •Alpha hyperparameter controls regularization strength; tune via k-fold cross-validation
• •Limitations: struggles with non-linear patterns, highly correlated features, and outliers

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