CART digs deep into your data, using advanced techniques to zero in on the optimal tree. CART uses a backwards-pruning technique that identifies the true structure of your data. This technique uses every record to alternately train and test, resulting in a highly reliable determination of optimal model complexity, while using all your data for learning. For larger databases, CART allows you to flexibly select test data, either choosing from separate test databases, defining test records by an identifying field, or randomly selecting test records. Most databases are plagued with missing data.
Older methodologies substitute the same value for all missing records.
A decision tree based on such methods will treat all missing records as if the true values were identical. In contrast, CART employs the unique concept of the surrogate, a variable that can be used as a stand-in when the variable of interest is missing. By using surrogates, CART handles each record individually, providing a far more accurate analysis. CART lets you specify penalties for each misclassification and uses this information to generate the decision tree. We don't think CART should be a mystery, so we include an in-depth comprehensive manual explaining the techniques.