06-01-2017 07:11 PM
I was wondering if someone can clear my concepts about a question?
I am using decision tree with standaridized data, in my previous that was whether I should used standardized data with decision tree or not, one of the members suggested that it wont hurt my model....
The problem I am facing is that few columns in both standardized or non standardized shape are harder to interpret. The difference standardization makes is to bring them in a range. Now atleast I know that my standardized data falls between 0 and 1.
Only thing to worry is "How should I interpret the results in Tree algorithms" ??
Should I maintain data dictionary with both Unique standardized and Non Standardized values and compare them or some other recommended way?
06-01-2017 07:28 PM
06-01-2017 07:33 PM
I think back transforming makes the most sense. The idea behind decision trees is rules humans can read in the end to give them a set of 'rules' to follow. The more difficult you make it the less likely that is to happen.