Logistic Regression, Stochastic Gradient, Regularization.
loss with L1: where d refers to the number of weights corresponding to the variables or columns in the dataset. In simple terms we add the sum of absolute values of all j weights derived and multiply it by constant lambda (λ) that controls the power of the regularization. Now if w’s are too large