Detrmining the BEST decision tree.

A couple quick points: --------- Do a quick google search on "Gains table", "Gains chart", and "lift chart" and you'll find some good info about comparing how good various models are. Independent of traditional measures of model performance (which typically look at performance across the full dataset), it's also possible that models that may not be ideal for some purposes, might still reveal some important findings or insights. E.g., a tree model might not do a great job overall, but it might identify a fraction of the data (a small terminal node) that has a very high percentage of targets. Depending on your domain, this small terminal node could be valuable (e.g., everyone in that group is likely to be committing tax fraud, or are likely to have cancer, etc.) --------- Also, just seeing which variables are important for the prediction can have value.