Abstract: A decision tree is a tree whose internal nodes can be taken as tests (on input data patterns) and whose leaf nodes can be taken as categories (of these patterns). These tests are filtered ...

Abstract: Because of their transparency, interpretability, and efficiency in classification tasks, decision tree algorithms are the foundation of many Business Intelligence (BI) and Analytics ...

Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, ...

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 over the years, ...

Machine learning holds the potential to solve many real-world problems, but interpretability is a necessary prerequisite for practitioners in high-stakes domains such as medicine and law. Decision ...

Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data—e.g., given a ...

ABSTRACT: The information gained after the data analysis is vital to implement its outcomes to optimize processes and systems for more straightforward problem-solving. Therefore, the first step of ...