There are many other techniques for binary classification, but logistic regression was one of the earliest developed and the technique is considered a fundamental machine learning skill for data ...

However, coding multi-class logistic regression from scratch has least four advantages over using a library. Your code can be small and efficient, you can avoid licensing and copyright issues, you ...

The LOGISTIC and PROBIT procedures can perform logistic and ordinal logistic regression. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," Chapter 39, "The LOGISTIC Procedure," ...

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

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual ...

We consider the case-control problem with stratified samples and assume a logistic model that does not include terms for strata, i.e., for fixed covariates the (prospective) probability of disease ...

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.