NeurEco can build an extremely effective model just using the data provided by the user, without changing any of the building parameters. However, the right normalization, based on the knowledge of the data’s nature, makes a big difference in the final model performance.

Output normalization is particularly sensitive as it can change the cost function.

Set outputs_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 outputs_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 outputs_shifting and outputs_scaling to ‘none’).