Understanding Logistic Regression Logistic regression is best explained by example. Suppose that instead of the Patient dataset you have a simpler dataset where the goal is to predict gender from x0 = ...

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

Regression can be used on categorical responses to estimate probabilities and to classify.

Logistic regression enables you to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome, or response, can be dichotomous (yes, no) or ordinal (low ...

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to ...

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

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.

Thomas R. Fears, Charles C. Brown, Logistic Regression Methods for Retrospective Case-Control Studies Using Complex Sampling Procedures, Biometrics, Vol. 42, No. 4 ...