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 ...

A hierarchical logistic regression model is proposed for studying data with group structure and a binary response variable. The group structure is defined by the presence of micro observations ...

As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems. Logistic regression is a technique used to make ...

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 ...

Outcome-dependent sampling increases the efficiency of studies of rare outcomes, examples being case-control studies in epidemiology and choice-based sampling in econometrics. Two-phase or double ...

SimpleNomo, an open-source Python Toolbox, and an online platform that generates nomograms directly from logistic regression coefficients and the range of variables are available through a recent ...