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I'm not certain how to interpret the entropy output--though I have used the Gini criterion before and interpreted it as the probability of reaching 100% on any given leaf the tree splits on.Though after reviewing things on the internet--I think I've been interpreting results really wrong for the decision tree altogether.

If I have a regression model, I would interpret a coefficient as saying the coefficient of coffee increases the probability that a person will be ready for work in the morning by 42%, if 0.42 were the coefficient on my summary out put.

But if it's on my Decision Tree and Entropy = 0.42--is that still associated with the probability of a binary dependant variable, with a sample = 500 and value [200, 300]

All of this is made up because I can't give you my actual data

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