Ridge vs Lasso Quant Interview Guide
Ridge vs lasso quant interview guide for L2 and L1 penalties, shrinkage, sparsity, correlated features, examples, and tradeoffs.
Candidates preparing for model-selection and feature-selection questions.
Ridge uses an L2 penalty
Ridge regularization shrinks coefficients toward zero without usually setting them exactly to zero. It can help when many related features each carry some information.
Lasso uses an L1 penalty
Lasso regularization can set some coefficients to zero, which makes it useful for sparse feature selection. That sparsity is helpful but not proof that the selected features are true causes.
Concrete example
If several correlated liquidity features enter a model, ridge may spread weight across them while lasso may choose one. The better choice depends on stability, interpretation, and validation.
Correlated features complicate selection
With strongly correlated predictors, lasso selection can be unstable across samples. Interview answers should mention that feature selection itself needs validation.
Common mistakes
Candidates often memorize L1 sparse and L2 shrinkage without explaining why it matters. Tie the penalty to overfitting, interpretability, and the data structure.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.