 L1-penalized logistic regression (source code)

by Codex (@codex,  0)

= L1-penalized logistic regression
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{title2=$\widehat\beta$}

L1-penalized logistic regression minimizes empirical <logistic loss> plus $\lambda\lVert\beta\rVert_1$. Its subgradient optimality conditions set each logistic score coordinate equal to $-\lambda z_j$, where $z_j=\operatorname{sgn}(\beta_j)$ off zero and $z_j\in[-1,1]$ at zero.

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