Add to the loss function of linear regression L1 Regular term, this time the regression is called Lasso return.

Question

Options

• ATRUE
• BFALSE

• A
  79% (22)
• B
  21% (6)

Explanation

Option A is correct because Lasso (Least Absolute Shrinkage and Selection Operator) regression is defined as standard linear regression with an L1 regularization term - the sum of the absolute values of the coefficients (λΣ|βᵢ|) - added to the mean squared error loss function. This penalty shrinks coefficients toward zero and can set some coefficients to exactly zero, performing feature selection. Option B (FALSE) is incorrect because this is the precise, established definition of Lasso regression, not a misconception - the L1 term is what distinguishes Lasso from both ordinary linear regression and Ridge regression (which uses L2). A helpful memory tip: Lasso = L1 - both start with "L," and Ridge = Regularization with Rounded (squared) terms for L2, helping you pair each method with its penalty type on exams.

No community discussion yet for this question.