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.

Because the logistic regression model was trained using normalized and encoded data, the x-input must be normalized and encoded in the same way. Notice the double square brackets on the x-input.

If a logistic regression model is trained for too many epochs, the model will overfit, meaning the model will predict very well for the training data, but predict poorly for the test data.

In this article, an exact conditional goodness-of-fit test for the logistic regression model with grouped binomial response data is proposed. Two efficient algorithms are presented for carrying out ...

Logistic regression was used to develop a risk prediction model using the FIT result and screening data: age, sex and previous screening history.

Course Topics"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 ...

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.

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...