  This work presents a new classifier that is specifically designed to be fully
interpretable. This technique determines the probability of a class outcome,
based directly on probability assignments measured from the training data. The
accuracy of the predicted probability can be improved by measuring more
probability estimates from the training data to create a series expansion that
refines the predicted probability. We use this work to classify four standard
datasets and achieve accuracies comparable to that of Random Forests. Because
this technique is interpretable by design, it is capable of determining the
combinations of features that contribute to a particular classification
probability for individual cases as well as the weightings of each of
combination of features.
