Individuals who used smokeless tobacco and e-cigarettes had an increased risk of developing age-related macular degeneration ...

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.

The Data Science Lab How to Do Kernel Logistic Regression Using C# Dr. James McCaffrey of Microsoft Research uses code samples, a full C# program and screenshots to detail the ins and outs of kernal ...

The Data Science Lab Logistic Regression Using PyTorch with L-BFGS Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression ...

Medical datasets often present a major challenge for machine learning models: skewness in continuous variables such as age, ...

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 ...

Multivariate regression models are applied to binary disease data in families identified from case-control studies. Attention is restricted to `marginal' or reproducible models, i.e. those whose ...

Estimation is considered for the class of conditional logistic regression models for clustered binary data proposed by Qu et al. (Communications in Statistics, Series A 16, 3447-3476, 1987) when ...