  Often machine learning methods are applied and results reported in cases
where there is little to no information concerning accuracy of the output.
Simply because a computer program returns a result does not insure its
validity. If decisions are to be made based on such results it is important to
have some notion of their veracity. Contrast trees represent a new approach for
assessing the accuracy of many types of machine learning estimates that are not
amenable to standard (cross) validation methods. In situations where
inaccuracies are detected boosted contrast trees can often improve performance.
A special case, distribution boosting, provides an assumption free method for
estimating the full probability distribution of an outcome variable given any
set of joint input predictor variable values.
