I was training a Logistic Regression model over a fairly large dataset with ~1000 columns.
I did apply scaling of features using MinMaxScaler.
I was wondering how to interpret the coefficients generated by the model and find something like feature importance in a Tree based model.
Should I re-scale the coefficients back to original scale to interpret the model properly?
It will be great if someone can shed some light on how to interpret the Logistic Regression coefficients correctly.