I am running a regression analysis on a 7000 row dataset with a train/test split of 70%/30%. I am using one variable
X to predict a variable
Xranges between 300 and 810 (mean 712).
Yis an integer (number of occurrences) ranging between 0 and 20 (mean 0.2).
Without standardisation or normalising
X, I receive:
Train score: 0.082 Test score: 0.077
However upon normalising (
X = (X-X.min())/(X.max()-X.min())), I receive:
Train score: 0.0000 Test score: -0.0001
Is there something incorrect about normalising for a Lasso regression? The same applies to standardising the data. Would anyone be able to advise me on the best course of action?