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 ...

The model was constructed on the basis of complete-case analysis. Simple logistic regression was used to identify potential predictors for paclitaxel HSR. Variables with a P value of <.05 were then ...

The model can naturally account for sampling variabilities and zero observations and also allow for a flexible covariance structure among the bacterial taxa. In order to select the relevant covariates ...