Unlike linear regression 1, which yields an exact analytical solution for the estimated regression coefficients, logistic regression requires numerical optimization to find the optimal estimate ...

This article will cover the basic theory behind logistic regression, the types of logistic regression, when to use them and take you through a worked example.

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

Motivated by this speedup, we propose modeling logistic regression problems algorithmically with a mixed integer nonlinear optimization (MINLO) approach in order to explicitly incorporate these ...

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 ...

A new study investigated how logistic regression model training affects performance, and which features are best to include when examining datasets from individuals suffering from COVID-19.

Misclassification of binary outcome variables is a known source of potentially serious bias when estimating adjusted odds ratios. Although researchers have described frequentist and Bayesian methods ...