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 …

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 …

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 …

Geological disasters on the slopes of open-pit mine dumps in energy extraction fall into the
category of mine production process safety. For the mine safety, it is crucial to accurately …

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 …

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

Many real-life applications consider nominal categorical predictor variables that have a
hierarchical structure, eg economic activity data in Official Statistics. In this paper, we focus …

We propose smooth monotone concave probabilistic regression trees for the estimation of
efficiency and productivity. In particular we modify these techniques to allow for the use of …

Due to the increasing use of Machine Learning models in high stakes decision making
settings, it has become increasingly important to have tools to understand how models arrive …