We present a machine learning algorithm for building classifiers that are comprised of a
small number of short rules. These are restricted disjunctive normal form models. An …

One of the most important obstacles to deploying predictive models is the fact that humans
do not understand and trust them. Knowing which variables are important in a model's …

We present an algorithm for building probabilistic rule lists that is two orders of magnitude
faster than previous work. Rule list algorithms are competitors for decision tree algorithms …

Interpretable classifiers have recently witnessed an increase in attention from the data
mining community because they are inherently easier to understand and explain than their …

It is extremely important in many application domains to have transparency in predictive
modeling. Domain experts do not tend to prefer" black box" predictive model models. They …

Techniques for interpretable machine learning Page 1 68 COMMUNICATIONS OF THE
ACM | JANUARY 2020 | VOL. 63 | NO. 1 review articles MACHINE LEARNING IS …

In this paper we present an average-case analysis of the Bayesian classi er, a simple
probabilistic induction algorithm that fares remarkably well on many learning tasks. Our …

In this paper, we review the induction of simple Bayesian classifiers, note some of their
drawbacks, and describe a recursive algorithm that constructs a hierarchy of probabilistic …

We present the design and implementation of a custom discrete optimization technique for
building rule lists over a categorical feature space. Our algorithm produces rule lists with …

In many e-commerce applications, ranging from dynamic Web content presentation, to
personalized ad targeting, to individual recommendations to the customers, it is important to …