Sensitivity analysis of the result in binary decision trees

This paper proposes a new method to qualify the result given by a decision tree when it is used as a decision aid system. When the data are numerical, we compute the distance of a case from the decision surface. This distance measures the sensitivity of the result to a change in the input data. With a different distance it is also possible to measure the sensitivity of the result to small changes in the tree. The distance from the decision surface can also be combined to the error rate in order to provide a context-dependent information to the end-user.