Input data normalization

Hi there,

yes, in my experience it is an important step so that the training works effectively and all features are treated equally within your training process. Usually you want this. E.g. for gradient-based methods this also helps to reach the optimum more quickly. I recently read a nice outline here:

Personally, I made good experience with giving it in advance some thought how the features are distributed in reality in order to chose the right method for normalising the range, e.g.

  • z Transformation / standardization for normally distributed features
  • min/max scaling for e.g uniform distributed features

Hope that helps!