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 ...

Figure 11.14: Logistic Regression: Model Dialog, Model Tab Figure 11.14 displays the Model dialog with the terms age, ecg, sex, and their interactions selected as effects in the model.. Note that you ...

And there are many ways to train a logistic regression model; one of the most common is called the L-BFGS algorithm. [Click on image for larger view.] Figure 1. Predicting an Employee's Gender Using ...

This book also explains the differences and similarities between the many generalizations of the logistic regression model. The following topics are covered: binary logit analysis, logit analysis of ...

This is problematic because an odds ratio always overestimates the risk ratio, and this overestimation becomes larger with increasing incidence of the outcome.5 There are alternatives for logistic ...

When training a logistic regression model, there are many optimization algorithms that can be used, such as stochastic gradient descent (SGD), iterated Newton-Raphson, Nelder-Mead and L-BFGS. This ...

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.

Nicholas J. Horton, Nan M. Laird, Maximum Likelihood Analysis of Logistic Regression Models with Incomplete Covariate Data and Auxiliary Information, Biometrics, Vol. 57, No. 1 (Mar., 2001), pp. 34-42 ...