The feature set for my multi-class multi-label classification task, using the MLPClassifier from scikit learn, contains mostly features where the values are in the same range of [0,1], but there are 3 out of 45 features where this isn't the case and feature scaling is required. So far I've tried out min-max normalization, mean normalization and z-score normalization on these features. However all scaling methods result in slightly different train and test performance and z-score standardization results in the fastest convergence but worst scores overall. To measure performance, Precision, Recall, F1 and MCC were used.
What is a decent strategy when choosing a type of feature scaling?