So, in the case of a binary logistic regression model, the dependent variable is a logit of p, with p being the probability that the dependent variables have a value of 1.

Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...

A class of conditional logistic regression models for clustered binary data is considered. This includes the polychotomous logistic model of Rosner (1984) as a special case. Properties such as the ...

Logistic regression is one of many machine learning techniques for binary classification -- predicting one of two possible discrete values. An example is predicting if a hospital patient is male or ...

A machine learning model may help predict mortality for hospitalized patients with cirrhosis at the time of admission, ...

The nature of outliers in the context of binary regression data is discussed. Resistant fitting procedures produce estimated regression coefficients which are numerically larger than maximum ...

The Data Science Lab How to Do Logistic Regression Using ML.NET Microsoft Research's Dr. James McCaffrey show how to perform binary classification with logistic regression using the Microsoft ML.NET ...

Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population ...