Logistic Regression, Machine Learning, Classification, Regularization, Algorithms.
Maximum Likelihood function Here, L(W) is the Maximum Likelihood Function. Taking product common for both parts Taking natural log on both sides we get: Log converts product to summation Here, l(W) is the Log of Likelihood. Since, p(Xᵢ) = σ(W.Xᵢ) and σ(W.Xᵢ) = 1/(1- e^(-W.Xᵢ)), We can rewrite the