What makes CART so easy to interpret?
As illustrated above, the results of a decision-tree data-mining project are displayed as a tree-shaped visual diagram. Discovered relationships and patterns in the data - even in massively complex datasets with hundreds of variables - are presented as a flow chart. Compare this to complex parameter coefficients in a logistic regression output or a stream of numbers in a neural-net output, and the appeal of decision trees is readily apparent.
The visual display enables users to see the hierarchical interaction of the variables. In addition, the display often confirms previous knowledge about important data relationships, which adds confidence in the reliability and utility of the CART model. Further, because simple if-then rules can be read right off the tree, models are easy to grasp and easy to apply to new data.