As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

Bankruptcy prediction has traditionally relied on statistical approaches such as Altman’s Z-score, which use financial ratios ...

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 ...

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 ...

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 ...

Multicenter Phase I/II Study of Cetuximab With Paclitaxel and Carboplatin in Untreated Patients With Stage IV Non–Small-Cell Lung Cancer Data from 1,066 patients recruited from nine European centers ...

Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...

We present an approach for correcting for interobserver measurement error in an ordinal logistic regression model taking into account also the variability of the ...