Example of recursive partitioning and evaluation of classification trees. First, we recursively divided the data set into patient subgroups and, for each subgroup, learned two logistic regression models for prediction of P(Restenosis | DES) and P(Hazard | DES), respectively. A pair of patient-subgroup-specific classifiers is denoted as solid rectangle on the left. Next, we constructed different classification trees, corresponding to different partitionings of the data set, by combining different patient-subgroup-specific classifiers to be applicable to the entire data set (dashed boxes on the left). In total, we created and evaluated n = 47 different trees. Finally, we selected the single best performing classification tree. The performance of each classification tree was the aggregated performances of its components (dashed box on the right). We used positive predictive value for the restenosis classifiers (red) and negative predictive value for hazardous events classifiers (blue). The mean of both values denotes the overall performance (bold). In the confusion matrices on the right, the first row (red) contains true positives and false positives with respect to P(Restenosis | DES), and the second row (blue) false negatives and true negatives with respect to P(Hazard | DES). Numbers do not represent actual results but only serve to illustrate our approach.