  A key prerequisite to optimal reasoning under uncertainty in intelligent
systems is to start with good class probability estimates. This paper improves
on the current best probability estimation trees (Bagged-PETs) and also
presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using
several benchmark datasets and multiple metrics. These experiments show that
MOB-ESP outputs significantly more accurate class probabilities than either the
baseline BPETs algorithm or the enhanced version presented here (EB-PETs).
These results are based on metrics closely associated with the average accuracy
of the predictions. MOB-ESP also provides much better probability rankings than
B-PETs. The paper further suggests how these estimation techniques can be
applied in concert with a broader category of classifiers.

Machine Learning;Artificial Intelligence;Machine Learning