Binary Logistic Regression: Binary logistic regression is employed when the dependent variable has only two outcomes—in this case, the dependent variable is referred to as a dichotomous variable.

Binomial logistic regression, where the outcome is binary (e.g. death, yes/no) is often simply referred to as logistic regression and will be the focus of this article. For example, a team of medical ...

There are many machine learning techniques that can be used for a binary classification problem; one of the simplest is called logistic regression. And there are many ways to train a logistic ...

Logistic Regression Using Python. The data doctor continues his exploration of Python-based machine learning techniques, explaining binary classification using logistic regression, which he likes for ...

"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count data. The most ...

The models used are binary logistic regression models based on the full sample of U.S. adults surveyed for this study. The analyses are based on the weighted sample, thus adjusting for differences in ...