What is difference between SGD classifier and SGD regressor in python?
Well, it's in the name. SGD Classifier is a model that is optimized (trained) using SGD (taking the gradient of the loss of each sample at a time and the model is updated along the way) in classification problems. It can represent a variety of classification models (SVM, logistic regression...) which is defined with the loss parameter. By default, it represents linear SVM. SGD Regressor is a model that is optimized (trained) using SGD  for regression tasks. It's basically a linear model that is updated along the way with a decaying learning rate.
What is difference between SGD classifier and SGD regressor in python sklearn?  Also can we set batch size for faster performance in them?