I have a dataset with 19 features and one binary label (Error: Yes/No).
What I would like to find are the maximum values for each of the 19 features, where crossing them would mean having an unacceptably high risk of the "Error" value being a "Yes".
I'd do this, for example, by calculating the AUC of a feature based on a SVM or logistic regression algorithm and define the "optimal cutoff" of the feature as 0.5 of the AUC. And I'd do this repeatedly for each of the features.
Does this approach make good statistical sense?