Accuracy, Precision, and F1 Score. Data practitioners can use the numbers derived from a confusion matrix to calculate their logistic regression models’ accuracy, recall, and F1 score.

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

Delta-p statistics is an easier means of communicating results to a non-technical audience than the plain coefficients of a logistic regression model. In this article, authors Maarit Widmann and ...

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.

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 ...

Abstract. Logistic regression models are commonly used to study the association between a binary response variable and an exposure variable. Besides the exposure of interest, other covariates are ...

Robert D. Gibbons, Donald Hedeker, Random Effects Probit and Logistic Regression Models for Three-Level Data, Biometrics, Vol. 53, No. 4 (Dec., 1997), pp. 1527-1537 Link account to institutional ...

Logistic Regression Model to Distinguish Between the Benign and Malignant Adnexal Mass Before Surgery: A Multicenter Study by the International Ovarian Tumor Analysis Group . JCO 23 , 8794-8801 (2005) ...