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A regularization method for regression models that shrinks coefficients towards zero.

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How is L1 regularization derived?

Equation (3) from the paper states: $$RSS = \|Xw - y\|_2^2$$ This is the "residual sum of squared errors" that we want to minimize. Differentiating $RSS$ with respect to the parameter vector $w$ (so …
Aaron Voelker's user avatar