Accuracy, Precision, and F1 Score. Data practitioners can use the numbers derived from a confusion matrix to calculate their logistic regression models’ accuracy, recall, and F1 score.

The Data Science Lab. Logistic Regression Using PyTorch with L-BFGS. Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression ...

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.

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

"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 ...

Previous methods struggle to incorporate real-time data or account for nonlinear interactions among macroeconomic variables.

The Data Science Lab. How to Do Multi-Class Logistic Regression Using C#. Dr. James McCaffrey of Microsoft Research uses a full code program, examples and graphics to explain multi-class logistic ...

When the dependent variable is categorical, a common approach is to use logistic regression, a method that takes its name from the type of curve it uses to fit data.

Example 39.9: Conditional Logistic Regression for Matched Pairs Data. In matched case-control studies, conditional logistic regression is used to investigate the relationship between an outcome of ...