Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

In any given machine learning problem, there may be many models that could explain the
data almost equally well. However, most learning algorithms return only one of these …

Over the past several decades, metal Additive Manufacturing (AM) has transitioned from a
rapid prototyping method to a viable manufacturing tool. AM technologies can produce parts …

Decision tree algorithms have been among the most popular algorithms for interpretable
(transparent) machine learning since the early 1980's. The problem that has plagued …

Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

Decision tree optimization is notoriously difficult from a computational perspective but
essential for the field of interpretable machine learning. Despite efforts over the past 40 …

We consider the use of decision trees for decision-making problems under the predict-then-
optimize framework. That is, we would like to first use a decision tree to predict unknown …

Decision trees are among the most popular machine learning models and are used routinely
in applications ranging from revenue management and medicine to bioinformatics. In this …