As we have mentioned many times, the tree-structured approach handles both categorical and ordered variables in a simple and natural way. Classification trees sometimes do an automatic stepwise variable selection and complexity reduction. They provide an estimate of the misclassification rate for a test point. For every data point, we know which leaf node it lands in and we have an estimation for the posterior probabilities of classes for every leaf node. The misclassification rate can be estimated using the estimated class posterior.
Classification trees are invariant under all monotone transformations of individual ordered variables. The reason is that classification trees split nodes by thresholding. Monotone transformations cannot change the possible ways of dividing data points by thresholding. Classification trees are also relatively robust to outliers and misclassified points in the training set. They do not calculate an average or anything else from the data points themselves. Classification trees are easy to interpret, which is appealing especially in medical applications.