JSTOR Daily

Logistic Disease Incidence Models and Case-Control Studies

The probability of disease development in a defined time period is described by a logistic regression model. A model for the regression variable, given disease status, is induced and is applied to ...
technologynetworks

Logistic Regression

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...
The American Journal of Managed Care

Cost vs Accuracy: Choosing the Best ML Model for Vascular Calcification in CKD

Logistic regression is the most cost-effective model for medial vascular calcification classification, with a mean ICER of $278 using five low-cost features. Despite similar diagnostic accuracy, ...
JSTOR Daily

Estimation and Inference for Logistic Regression with Covariate Misclassification and Measurement Error in Main Study/Validation Study Designs

In epidemiological studies, continuous covariates often are measured with error and categorical covariates often are misclassified. Using the logistic regression ...
Visual Studio Magazine

How to Do Logistic Regression Using ML.NET

Microsoft Research's Dr. James McCaffrey show how to perform binary classification with logistic regression using the Microsoft ML.NET code library. The goal of binary classification is to predict a ...
Visual Studio Magazine

Logistic Regression Using PyTorch with L-BFGS

Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...