Thomas R. Fears, Charles C. Brown, Logistic Regression Methods for Retrospective Case-Control Studies Using Complex Sampling Procedures, Biometrics, Vol. 42, No. 4 ...

We used logistic regression as a method of sensitivity analysis for a stochastic population viability analysis model of African wild dogs (Lycaon pictus) and compared these results with conventional ...

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 ...

There are dozens of code libraries and tools that can create a logistic regression prediction model, including Keras, scikit-learn, Weka and PyTorch. When training a logistic regression model, there ...

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.

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end program that explains how to perform binary classification (predicting a variable with two possible discrete values) using ...

Course Objectives: This course covers nonparametric modeling of complex, nonlinear predictive relationships in data with categorical (classification) and numerical (regression) response variables. It ...