  Falling rule lists are classification models consisting of an ordered list of
if-then rules, where (i) the order of rules determines which example should be
classified by each rule, and (ii) the estimated probability of success
decreases monotonically down the list. These kinds of rule lists are inspired
by healthcare applications where patients would be stratified into risk sets
and the highest at-risk patients should be considered first. We provide a
Bayesian framework for learning falling rule lists that does not rely on
traditional greedy decision tree learning methods.
