My question is do we need to standardize the data set to make sure all variables have the same scale, between [0,1], before fitting logistic regression. The formula is:
My data set has 2 variables, they describe the same thing for two channels, but the volume is different. Say it's the number of customer visits in two stores, y here is whether a customer purchases. Because a customer can visit both stores, or twice first store, once second store before he makes a purchase. but the total number of customer visits for 1st store is 10 times larger than the second store. When I fit this logistic regression, without standardization,
coef(store1)=37, coef(store2)=13; if I standardize the data, then
coef(store1)=133, coef(store2)=11. Something like this. Which approach makes more sense?
What if I am fitting a decision tree model? I know tree structure models don't need standardization since the model itself will adjust it somehow. But checking with all of you.