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 ...

The random forest model significantly outperformed all other models, including the logistic regression model that the entire paper focuses on, with an eventual AUC of 0.936 and an accuracy of 0.918.

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are ...

Imrey, Koch, Stokes and collaborators (1981) have reviewed the literature of log linear and logistic categorical data modelling, and presented a matrix formulation of log linear models parallel to the ...

GLMs unify other statistical models, including gamma regression models appropriate for right skewed responses; logistic regression appropriate for categorical responses; and log-linear models ...

Data from 1,066 patients recruited from nine European centers were included in the analysis; 800 patients (75%) had benign tumors and 266 (25%) had malignant tumors. The most useful independent ...