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.

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

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are ...

Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies.

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 ...

The analysis of data generated by animal habitat selection studies, by family studies of genetic diseases, or by longitudinal follow-up of households often involves fitting a mixed conditional ...

The data doctor continues his exploration of Python-based machine learning techniques, explaining binary classification using logistic regression, which he likes for its simplicity.