- #1

fog37

- 1,568

- 108

- TL;DR Summary
- Understand when and when not to normalize the range of the independent variables...

Hello,

On the topic of feature scaling: I am wondering if normalization needs to be used all the time or only in some particular circumstances. Normalization means transforming/remapping the range of a variable with values ##[x_0,x_f]## to the range ##[0,1]##.

For example, let's consider a linear regression model with 3 independent variables and one dependent variable: $$Y= a X_1 +b X2 + c X3$$

It is generally likely that the independent variables ##X_1 , X_2, X_3## have very different ranges. For example, ##X_1## may have values between 0 and 2000 while ##X_3## value only between 0 and 0.5...Is that an issue? Would the variable with the largest range possibly influence the dependent variable ##Y## more significantly just because of its wider range and not because it is truly important? I don't see normalization being applied all the time...

Is it always good practice, no matter the model we are going for, to first normalize all the independent variables so they their values all fall within the same range?

Thank you!

On the topic of feature scaling: I am wondering if normalization needs to be used all the time or only in some particular circumstances. Normalization means transforming/remapping the range of a variable with values ##[x_0,x_f]## to the range ##[0,1]##.

For example, let's consider a linear regression model with 3 independent variables and one dependent variable: $$Y= a X_1 +b X2 + c X3$$

It is generally likely that the independent variables ##X_1 , X_2, X_3## have very different ranges. For example, ##X_1## may have values between 0 and 2000 while ##X_3## value only between 0 and 0.5...Is that an issue? Would the variable with the largest range possibly influence the dependent variable ##Y## more significantly just because of its wider range and not because it is truly important? I don't see normalization being applied all the time...

Is it always good practice, no matter the model we are going for, to first normalize all the independent variables so they their values all fall within the same range?

__Another possible issue__we may have with independent variables is that the may be pairwise linearly correlated: too much correlation is not good. How much correlation can we accept? Is the presence of such correlation not desirable because it leads to think of an actual correlation between ##Y## and, say, ##X_1##, just by proxy via another independent, say ##X_2## if ##X_1## and ##X_2## are correlated? I don't see a problem with that...Thank you!