A simple model with two variables [A,B] to train, let's say, a logistic regression or any other classification model:
- A: Flat distribution from 0 to 100.
- B: A logarithmic distribution from 0 to a few thousands.
What would be the proper way to normalize this? Should I make B flat before? Do I put a limit before the max in B and consider all points above like the max?
I read you carefully. Thanks in advance.
