# ROC for testing goodness of fit

I'm interested in using ROC to test for goodness of fit for binary models such as logistic regression.

I'm a bit confused by the literature where it is mostly just explained as a valid technique to test goodness of fit, and then how to calculate sensitivities, specificities etc. I'm more interested in the workflow of using ROC to test goodness of fit.

Say I have a multiple logistic regression model with one dependent y and 10 independent x1, x2...x10.

Where do I go from here? Do I split the data up and then compare ROC between them or? I'm just a bit confused on the next step.

• You would fit a model and then use the predicted probabilities to calculate sensitivities and specificities that make up the ROC curve. I am not sure what you mean about splitting up the data...some kind of cross validation?
– Dave
Oct 17 '20 at 13:42
• Instead of using the (area under the) ROC (AUROC) to evaluate model fit, consider a proper scoring rule like the Brier score, the equivalent of mean-square error for class-membership modeling. See this thread among others on this site. Although AUROC can be OK for evaluating a single model, it is not very sensitive for distinguishing reliably among different models.
– EdM
Oct 17 '20 at 14:22