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 ...

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 ...

The demo program trains the logistic regression model using an iterative process. Behind the scenes, the demo is using the gradient ascent log likelihood optimization technique (which, as you'll see, ...

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 ...

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 is one of many machine learning techniques for binary classification -- predicting one of two possible discrete values. An example is predicting if a hospital patient is male or ...

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.

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic ...

The random forest model significantly outperformed all other models, including the logistic regression model that the entire paper focuses on, with an eventual AUC of 0.936 and an accuracy of 0.918.