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How to interpret results when decision tree used with standardised data

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Contributor
Posts: 47

How to interpret results when decision tree used with standardised data

Hi,

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?

 

Regards

 

Super User
Posts: 19,770

Re: How to interpret results when decision tree used with standardised data

Posted in reply to geniusgenie

Standardizing is interesting with interpretations, can you transform back to make the rules interpretable?

Contributor
Posts: 47

Re: How to interpret results when decision tree used with standardised data

Hi Reeza, thanks for your reply, How should I transform back? I am little confused on this. I was thinking to refer back to data dictionary which shows non standardized values of the columns and compare them with standardized ones but still its rough idea.. not sure about it.
Super User
Posts: 19,770

Re: How to interpret results when decision tree used with standardised data

Posted in reply to geniusgenie

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. 

 

 

Contributor
Posts: 47

Re: How to interpret results when decision tree used with standardised data

I will give it a go and lets hope it gives me something good.

Thanks a lot
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