Abstract: Random Forest is a well-known type of ensemble learning, which combines a number of decision trees to improve the prediction ability and reduce the risk of overfitting. This paper aims at ...
Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.
Introduction: In the temperate grasslands of the UK, forage quality is a key factor influencing both animal performance and environmental impact. Because forage quality strongly affects rumen ...
ABSTRACT: Quantify global carbon stock in tropical forests to climate change mitigation requires availability of data and tools such as allometric models. The study aimed to estimate aboveground ...
Survival analysis applied to a breast cancer dataset, including information on hormone therapy, chemotherapy, radiotherapy, and time-to-event variables. The study uses Kaplan–Meier curves, GLM models, ...
In this project we aim to develop a regression model that uses sleep-related, lifestyle, and physiological information to predict an individual’s self-reported stress level. Research shows that 30–40% ...
Department of Orthopedics, Shanxi Bethune Hospital, Tongji Shanxi Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Taiyuan, China Background and aim: ...
Objective: This research applies Random Forest Regression(RFR) as a non-linear technique to forecast temperature based on multiple input parameters, including motor load, speed, and ambient ...
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