Regularization is how you stop a model from memorizing noise, and it is one of the most reliable interview topics because it ties together the bias-variance tradeoff, the geometry of norms, and the practical reality of overfitting. In finance, where the signal-to-noise ratio is brutal and features are often collinear, regularization is not optional: it is the difference between a model that survives out of sample and one that does not. This lesson covers the two core penalties, Ridge ($L_2$) and Lasso ($L_1$), explains why Lasso produces sparse models and Ridge does not, introduces Elastic Net, works a shrinkage example by hand, and shows how to choose the penalty strength. We tie each idea to the interview and to trading.
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