Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

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 ...

It was one Christmas visitor that didn’t overstay its welcome. A Burmese python that had been spotted in a Miami-Dade neighborhood was removed just days before Christmas after a resident out for a ...

This repository contains comprehensive implementations and solutions for statistical analysis, data science methodologies, and computational mathematics assignments. Each assignment demonstrates ...

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 ...

Background: Stanford Type A aortic dissection (TAAD) is a life-threatening condition involving the ascending aorta and requires urgent surgery. This study developed 11 machine learning regression ...

Abstract: Gradient boosting is an efficient and scalable supervised machine learning technique, and most scaling models based on gradient boosting perform well on point regression tasks, but they can ...

If you’ve ever tried to build a agentic RAG system that actually works well, you know the pain. You feed it some documents, cross your fingers, and hope it doesn’t hallucinate when someone asks it a ...

Collective decision-making is hardly a perfect science. Broken processes, data overload, information asymmetry, and other inequities only compound the challenges that come from large, disparate ...