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 article provides a birds-eye view on the role of decision trees in machine learning and
data science over roughly four decades. It sketches the evolution of decision tree research …

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 …

We provide a new formulation for the problem of learning the optimal classification tree of a
given depth as a binary linear program. A limitation of previously proposed Mathematical …

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 …

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 …

Explanations of machine learning (ML) predictions are of fundamental importance in
different settings. Moreover, explanations should be succinct, to enable easy understanding …

Decision tree learning is a widely used approach in machine learning, favoured in
applications that require concise and interpretable models. Heuristic methods are …

Decision trees are among the most popular classification models in machine learning.
Traditionally, they are learned using greedy algorithms. However, such algorithms pose …