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

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 ...

Logistic regression enables you to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome, or response, can be dichotomous (yes, no) or ordinal (low ...

Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...

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.

Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design.

The log-logistic distribution has a non-monotonic hazard function which makes it suitable for modelling some sets of cancer survival data. A log-logistic regression model is described in which the ...

Mixed Effects Logistic Regression Models for Multiple Longitudinal Binary Functional Limitation Responses with Informative Drop-Out and Confounding by Baseline Outcomes Thomas R. Ten Have, Beth A.