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.

The term "logistic regression" encompasses logit modeling, probit modeling, and tobit modeling and the significance tests of their estimates. A total of 52 articles were identified as using logistic ...

With many nuisance parameters to eliminate and missing covariates, many investigators exclude any subject with missing covariates and then use conditional logistic regression, often called a ...

Logistic Regression Using the scikit Library Dr. James McCaffrey of Microsoft Research says the main advantage of scikit is that it's easy to use (even though most classes have many constructor ...

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

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

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.