Understand logistic regression, a key statistical method for relationships with binary outcomes. Explore its formula, assumptions and practical applications.

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.

Scott Menard, Six Approaches to Calculating Standardized Logistic Regression Coefficients, The American Statistician, Vol. 58, No. 3 (Aug., 2004), pp. 218-223 ...

S. Le Cessie, J. C. Van Houwelingen, Ridge Estimators in Logistic Regression, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 41, No. 1 ...

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems.

This article presents a complete demo program for logistic regression, using batch stochastic gradient descent training with weight decay. Compared to other binary classification techniques, logistic ...

A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams ...

What are the advantages of logistic regression over decision trees? This question was originally answered on Quora by Claudia Perlich.