Logistic Regression, Stochastic Gradient, Regularization.
and thus it would take longer to converge. Since k pts are chosen randomly we call it Stochastic Gradient Descent. note: sklearn SGDClassifier randomly shuffles all datapoints to create the randomness. When k = 1 the model is SGD and when 1 < k < n, the model is Mini Batch Gradient Descent. Now