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

The logistic regression model can be represented with the following formula: Where the left side of the equation is the probability the outcome variable Y is 1 given the explanatory variables X.

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

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.

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.