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 …

Road accidents are one of the most relevant causes of injuries and death worldwide, and
therefore, they constitute a significant field of research on the use of advanced algorithms …

Abstract Machine learning is currently one of the hottest topics that enable machines to learn
from data and build predictions without being explicitly programmed for that task …

Research has shown online learners' performance to have a strong association with their
demographic characteristics, such as regional belonging, socio-economic standing …

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

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

The goal of supervised learning is to build a concise model of the distribution of class labels
in terms of predictor features. The resulting classifier is then used to assign class labels to …

Business intelligence is a broad category of applications and technologies for gathering,
providing access to, and analyzing data for the purpose of helping enterprise users make …

Supervised classification is one of the tasks most frequently carried out by so-called
Intelligent Systems. Thus, a large number of techniques have been developed based on …

Decision trees have proved to be valuable tools for the description, classification and
generalization of data. Work on constructing decision trees from data exists in multiple …