Scaling features for neural network input I have a df with many features that take both negative and positive values.For example a feature may have values in range (-10 , 10).For each feature which has negative values the negative sign means direction and this -10 is actually a larger value than - 5.
After finilizing my model i tried many scaling techniques such as Minmaxscaler from (0,1) and mean max scaler from (-1,1),robust scaler,standarization and found the minmaxscaler from (0,1) has the best results.
My concern is this:
Scaling the data with this technique does not account for the true meaning of my feature as seen belo and -2 will be smaller than -1 after scaling to (0,1).
Is there a way to enforce this rule(negative means only direction) during my scaling process?

You could distinguish direction and magnitude by re-expressing the two components of your data format.

*

*$| x |$ captures magnitude, reflecting that $-10$ is "larger" than $-5$.

*the sign $\text{sgn}(x)$ captures directing, with $+1$ indicating one direction and $-1$ the opposite.

This will double the number of features you have, which might not be desirable.
