Feature scaling for MLP neural network sklearn I am working with a dataset that has multiple scales for my features. Before running sklearn's MLP neural network I was reading around and found a variety of different opinions for feature scaling. Some saying you need to normalize, some saying only standardize, others saying in theory nothing is needed for MLP, some saying only to scale training data and not testing data, the sklearn documentation says MLP is sensitive to feature scaling? This has left me very confused on which route I should take for my dataset before running the MLP model. Any clarification on these and how I should proceed would be much appreciated.
In short:

Scaling is indeed desired.
Standardizing and normalizing should both be fine. And reasonable scaling should be good.
Of course you do need to scale your test set, but you do not &quot;train&quot; (i.e. fit) your scaler on the test data - you scale them using a scaler fitted on the train data (it's very natural to do in SKLearn).

For example, if you're normalizing your data (like with an SKLearn StandardScaler object), you .fit it on the train data to get the mean and standard deviance from it, and you .transform both train and test data to subtract the train mean and divide by the standard deviance.