NeurEco can build an extremely effective model just using the data provided by the user, without changing any one of the building parameters.
However, the right normalization will make a big difference in the final model performance. 

Set **inputs_normalize_per_feature** to True if trying to fit targets of different natures (temperature and pressure for example) and want to give them equivalent importance.

Set **inputs_normalize_per_feature** to False if trying to fit quantities of the same kind (a set of temperatures for example) or a field.

If neither of these options suits the problem, normalize the data your own way prior to feeding them to NeurEco (and deactivate output normalization by setting **inputs_shifting** and **inputs_scaling** to *'none'*). 