What is the difference between SGDClassifier and LogisticRegression in Scikit-Learn?

SGDClassifier uses stochastic gradient descent optimization, while LogisticRegression uses more stable solvers like LBFGS or Newton methods.

LogisticRegression in Scikit-Learn solves the optimization problem using deterministic solvers (lbfgs, liblinear, saga), which converge steadily and are suitable for small to medium datasets. SGDClassifier instead uses stochastic gradient descent, updating weights per sample or mini-batch. This makes SGDClassifier scalable for very large datasets and online learning but introduces more variance in convergence behavior. Both can optimize logistic loss, but SGD is more flexible and scalable.