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.

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

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 ...

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

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.

Taxa count data in microbiome studies are often over-dispersed and include many zeros. To account for such an over-dispersion, we propose to use an additive logistic normal multinomial regression ...

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.

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