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

Users of logistic regression models often need to describe the overall predictive strength, or effect size, of the model's predictors. Analogs of R² have been developed, but none of these measures are ...

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort ...

Results from classic linear regression regarding the effect of adjusting for covariates upon the precision of an estimator of exposure effect are often assumed to apply more generally to other types ...