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 …

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

Decision trees have the particularity of being machine learning models that are visually easy
to interpret and understand. Therefore, they are primarily suited for sensitive domains like …

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 …

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

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 …

Several recent publications have studied the use of Mixed Integer Programming (MIP) for
finding an optimal decision tree, that is, the best decision tree under formal requirements on …

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 …

Decision trees (DTs) epitomize what have become to be known as interpretable machine
learning (ML) models. This is informally motivated by paths in DTs being often much smaller …