Which regularization technique simultaneously performs variable selection by producing exactly zero coefficients for irrelevant features?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — c is correct because Lasso uses an L1 penalty whose diamond-shaped constraint region has corners on coordinate axes, causing the optimal solution to land at corners where coefficients become exactly zero. A (Ridge/L2) shrinks coefficients toward but not exactly to zero.
Full explanation below image
Full Explanation
C is correct because Lasso uses an L1 penalty whose diamond-shaped constraint region has corners on coordinate axes, causing the optimal solution to land at corners where coefficients become exactly zero. A (Ridge/L2) shrinks coefficients toward but not exactly to zero. B (Elastic Net) combines L1 and L2 and can produce sparsity but is less aggressive. D (Dropout) is a neural network technique unrelated to coefficient selection.