Chunk #29 — 3 Regularized Logistic Regression

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Regularization Paths for Generalized Linear Models via Coordinate Descent.

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Here we fit this model by regularized maximum (binomial) likelihood. Let p(xi) = Pr(G = 1|xi) be the probability (11) for observation i at a particular value for the parameters (β0, β), then we maximize the penalized log likelihood (13)max(β0,β)∈ℝp+1[1N∑i=1N{I(gi=1)logp(xi)+I(gi=2)log(1−p(xi))}−λPα(β)].