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 ...

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.

Course Topics"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count ...

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 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 ...

First off, you need to be clear what exactly you mean by advantages. People have argued the relative benefits of trees vs. logistic regression in the context of interpretability, robustness, etc.

David Muchlinski, David Siroky, Jingrui He, Matthew Kocher, Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data, Political Analysis, Vol. 24, No. 1 ...

A. Azzalini, Logistic Regression for Autocorrelated Data with Application to Repeated Measures, Biometrika, Vol. 81, No. 4 (Dec., 1994), pp. 767-775 ...

In contrast to traditional epidemiology, logistic regression is often used in genome-wide association studies (GWAS) of cohort and case-cohort data to assess the associations of single-nucleotide ...