Gradient ascent (a) The log-likelihood for the binomial logistic regression in one dimension is given by: L(θ) = E[y(log(1 + e^(θ^T x)) + (1-y)(log(1 + e^(-θ^T x)))] Name one algorithm used to find the maximizing θ. The gradient ascent update for θ_k+1 is given by: θ_k+1 = θ_k + α ∇L(θ_k) What is the significance of α? Extra credit: Write/derive the two elements of ∇^2L(θ_k)
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Gradient ascent is an optimization algorithm used to find the maximum of a function. In the context of logistic regression, it is used to find the values of the coefficients (B) that maximize the log-likelihood function. Show more…
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What is logistic regression (I can find definition online so maybe you can skip this) Why do we need log odds? and how do we interpret the coefficients of a logistic regression? I know it's a bit different from and How is maximum likelihood used to find these coefficients. Is it the only way? please be ready for follow up questions Are there any assumptions that must be satisfied for logistic regression to be used?
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