What regularization technique penalizes the sum of the absolute values of the weights, often leading to sparse weight vectors?

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L1 regularization

L1 regularization, also known as Lasso, adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function. This unique property often shrinks less important feature weights to exactly zero, effectively performing automatic feature selection. A helpful way to remember this is that 'L1' uses the first-degree power of weights, which creates the diamond-shaped constraint that leads to sparsity.

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