I am struggling with a conceptual problem related to feature scaling.
Let's assume I am building a classifier (e.g., a NN) and let's assume I rely on future scaling for the input features of my model.
In this context I will normalise the training set using its mean and its std and I would do the same with the testing set using the testing mean and std.
Let us also assume I succeed in building my classifier and I move to production where I try to classify new inputs. However for such new inputs the mean and std are unknown! How can I scale them appropriately before processing with my model? May be I could use the mean and std from training+testing.....
I really don't know which is the correct practice here....any hint?
Thank you for your help!