Heteroscedasticity describes a situation where risk (variance) changes with the level of a variable. In financial models, ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

The purpose of the plot_distribution_linear_regression.html example is to present the distribution of the estimators in linear regression. The first part presents the distribution of the sample ...

ABSTRACT: This research aims to develop reliable models using machine learning algorithms to precisely predict Total Dissolved Solids (TDS) in wells of the Permian basin, Winkler County, Texas. The ...

1 Department of Animal Sciences, University of Florida, Gainesville, FL, United States 2 Department of Animal Science, Iowa State University, Ames, IA, United States Background: To address the ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Nowadays, frontiers among different sciences are revealed as diffuse, and as a ...