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.

We used logistic regression as a method of sensitivity analysis for a stochastic population viability analysis model of African wild dogs (Lycaon pictus) and compared these results with conventional ...

To compare logistic regression (LR) with machine learning (ML) techniques for prediction of noncardia gastric cancer using electronic health records derived from two multiethnic populations.

Logistic regression has recently been advanced as a viable procedure for detecting differential item functioning (DIF). One of the advantages of this procedure is the considerable flexibility it ...

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

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end program that explains how to perform binary classification (predicting a variable with two possible discrete values) using ...

Classify: Weka trains and tests learning schemes that perform classification or regression. The classifiers can be divided into Bayesian, trees, rules, functions and lazy.