There is indeed a vast literature on the design and analysis of decision tree algorithms that aim at optimizing these parameters. This paper contributes to this important line of research: we propose ...

Decision trees have played a particularly important role in machine learning tasks that require explainability, interpretability, and transparency, such as medical healthcare diagnosis and disease ...

Abstract: Big Data enforces the usage of data mining techniques to provide the user valuable insights. There is a broad range of data mining and machine learning techniques tackling different tasks.

Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...

1 School of Nursing, Naval Medical University, Shanghai, China 2 Department of Clinical Psychology, Chongqing Mental Health Center, Chongqing, Shanghai, China Background: Benefit finding (BF) improves ...

Tsukuba, Japan—Data visualization has emerged as a powerful tool for enabling data-driven decision-making across diverse domains, including business, medicine, and scientific research. However, no ...

Junior faculty are often told to protect their time, but nobody provides instructions for how to do so. As an assistant professor at a public university, I have struggled to balance my course load, my ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Decision-making during the early stages of research and development (R&D) should be ...

Add a description, image, and links to the tree-visualization topic page so that developers can more easily learn about it.

The objective of this task is to implement and compare Decision Tree and Random Forest classification models on a healthcare dataset (Heart Disease). The goal is to evaluate their accuracy, understand ...