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Medium · Linear Algebra & Machine Learning · Quant Trader interview question · regularization, lasso, ridge, sparsity, machine-learning
You're building a predictive model for stock returns using a large number of technical indicators. You want to use either LASSO (L1 regularization) or Ridge (L2 regularization) regression to prevent overfitting and improve generalization. Why does LASSO regression tend to produce sparse solutions (i.e., models with many coefficients exactly equal to zero) while Ridge regression typically does not?