Medical datasets often present a major challenge for machine learning models: skewness in continuous variables such as age, ...

Gary King, Langche Zeng, Logistic Regression in Rare Events Data, Political Analysis, Vol. 9, No. 2 (Spring 2001), pp. 137-163 ...

We discuss the use of standard logistic regression techniques to estimate hazard rates and survival curves from censored data. These techniques allow the statistician to use parametric 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.

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 ...

The Data Science Lab Logistic Regression with Batch SGD Training and Weight Decay Using C# Dr. James McCaffrey from Microsoft Research presents a complete end-to-end program that explains how to ...

Because many common DIF detection methods ignore potential DIF variance, this paper proposes the use of random coefficient hierarchical logistic regression (RC-HLR) models to test for both uniform DIF ...

Data from 1,066 patients recruited from nine European centers were included in the analysis; 800 patients (75%) had benign tumors and 266 (25%) had malignant tumors. The most useful independent ...

A Refresher on Regression Analysis Understanding one of the most important types of data analysis. by Amy Gallo November 4, 2015 uptonpark/iStock/Getty Images ...