A normalization operation for NeurEco is a combination of a \(shift\) and a \(scale\), so that:

\[x_{normalized} = \frac{x-shift}{scale}\]

Allowed shift methods for NeurEco and their corresponding shifted values are listed in the table below:

NeurEco Tabular shifting methods

Name	shift value
none	\[0\]
min	\[min(x)\]
min_centered	\[0.5 * (min(x) + max(x))\]
mean	\[mean(x)\]

Allowed scale methods for NeurEco Tabular and their corresponding scaled values are listed in the table below:

NeurEco Tabular scaling methods

Name	scale value
none	\[1\]
max	\[max(x) - shift\]
max_centered	\[0.5 * (max(x) - min(x))\]
std	\[std(x)\]

Normalization with auto options:

• shift is mean and scale is max if the value of mean is far from 0,

• shift is none and scale is max if the calculated value of mean is close to 0

If the normalization is performed by feature, and the auto options are chosen, the normalization is performed by group of features. These groups are created based on the values of mean and std.