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 …

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 …

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 …

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 sets and decision lists are two of the most easily explainable machine learning
models. Given the renewed emphasis on explainable machine learning decisions, both of …

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 …

Regression trees are one of the oldest forms of AI models, and their predictions can be
made without a calculator, which makes them broadly useful, particularly for high-stakes …