The Single_Tree_Drive function creates a single classification decision tree. The function supports numeric variables and categorical attributes, handles missing values during the prediction phase, and supports GINI, entropy, and chi-square impurity measurements.
You can use the Single Decision Tree functions to generate predictions input for the Receiver Operating Characteristic (ROC) function. See Example 2: Creating Input for ROC.
The implementation of probability estimation trees (frequency based probability calculation for class assignment and laplace correction) is according to "Tree Induction for Probability based Ranking, Provost and Domingos", Provost and Domingos (2002) (http://homes.cs.washington.edu/~pedrod/papers/mlj03a.pdf).