  This paper describes techniques for growing classification and regression
trees designed to induce visually interpretable trees. This is achieved by
penalizing splits that extend the subset of features used in a particular
branch of the tree. After a brief motivation, we summarize existing methods and
introduce new ones, providing illustrative examples throughout. Using a number
of real classification and regression datasets, we find that these procedures
can offer more interpretable fits than the CART methodology with very modest
increases in out-of-sample loss.
