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 …

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

Decision trees of low depth are beneficial for understanding and interpreting the data they
represent. Unfortunately, finding a decision tree of lowest complexity (depth or size) that …

Decision trees are one of the most useful and popular methods in the machine learning
toolbox. In this paper, we consider the problem of learning optimal decision trees, a …

Rule sets are highly interpretable logical models in which the predicates for decision are
expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model …

Artificial Intelligence (AI) is widely used in decision making procedures in myriads of real-
world applications across important practical areas such as finance, healthcare, education …

One favors decision trees (DTs) of the smallest size or depth to facilitate explainability and
interpretability. However, learning such an optimal DT from data is well-known to be NP …

Interpretable and fair machine learning models are required for many applications, such as
credit assessment and in criminal justice. Decision trees offer this interpretability, especially …

The literature has shown how to optimize and analyze the parameters of different types of
neural networks using mixed integer linear programs (MILP). Building on these …