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 ...

Tree-based models are a foundational class of supervised machine learning algorithms that partition input data into branches based on feature values, creating an intuitive, rule-based tree structure.

In order to improve the accuracy and efficiency of sports training data analysis, this paper proposes an optimized analysis model by combining Iterative Dichotomiser 3 (ID3) decision tree algorithm ...

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, ...

Data mining is a crucial field in the era of big data, enabling the extraction of valuable patterns and knowledge from large datasets. Among the various techniques used in data mining, 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 ...

In recent years, artificial intelligence has played an important role in education, wherein one of the most commonly used applications is forecasting students’ academic performance based on personal ...

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 ...