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 ...

Mr. Currell was a deputy undersecretary and senior adviser at the Department of Education from 2018 to 2021. He is a trustee of Gustavus Adolphus College in St. Peter, Minn. This week, about 200,000 ...

College of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian, Liaoning 116029, China ...

Abstract: This paper proposes a production decision analysis model based on decision tree and Bayesian optimisation, aiming to optimise the decision-making in the production process of enterprises.

Objective: To develop a decision tree model using clinical risk factors to predict massive pulmonary hemorrhage (MPH) and MPH-related mortality in extremely low birth weight infants (ELBWIs). Method: ...

Decision trees are a powerful tool for decision-making and predictive analysis. They help organizations process large amounts of data and break down complex problems into clear, logical steps. Used in ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of decision tree regression using the C# language. Unlike most implementations, this one does not use recursion ...

Objectives This study aims to determine whether machine learning can identify specific combinations of long-term conditions (LTC) associated with increased sarcopenia risk and hence address an ...

Agricultural Yield and Population Forecasting: Utilizing machine learning to enhance crop yield and predict global population trends, informing sustainable development and policy decisions.

Abstract: The study develops an intelligent decision support system using point-of-care ultrasound imaging. The system’s primary emphasis is on tackling the issues that might arise in healthcare ...