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 ...

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 ...

Data Science expert with desire to help companies advance by applying AI for process improvements. The journey to Kaggle’s winning approach started in the mid-20th century, and its development has ...

Abstract: In the era of Big Data where voluminous data is handled on a very large scale, traditional decision trees might be very time consuming and sometimes might even fail to work owing to its ...

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 ...

Pruning is essential in tree-based machine learning models to mitigate overfitting caused by excessive features and noise. Decision trees utilise a hierarchical structure to effectively partition data ...