What is the value of the area under the ROC curve AUC to conclude that a classifier is excellent?

What is the value of the area under the ROC curve AUC to conclude that a classifier is excellent?

What is the value of the area under the roc curve (AUC) to conclude that a classifier is excellent? The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier.

What is the relationship between sensitivity and specificity?

Sensitivity refers to a test’s ability to designate an individual with disease as positive. A highly sensitive test means that there are few false negative results, and thus fewer cases of disease are missed. The specificity of a test is its ability to designate an individual who does not have a disease as negative.

What should sensitivity be on ROC curve for AUC?

Sensitivity be on Y-axis and (1 – Specificity) on X-axis. This plot is ROC Curve. Let us say, we consider the threshold cut-off to be 0. If the predicted probability is greater than or equal to 0 then Positive else Negative. In this case, all observations will be classified as Positive.

When is AUC is likely to be high?

If that is pretty much the definition, then would that mean if your sensitivity and specificity are high (e.g. both are higher than say 80%) then AUC is likely to be high also? Let’s say I’m building a model that’s trying to classify whether something is a cat or dog. Could someone explain the concept of AUC and relate it to this example?

What is the accuracy of the AUC model?

Even without a model, if we classify all cases as No-Fraud, the model classification accuracy would be 99%. In such a scenario, we may preferably use model performance measures like AUC, precision, recall, specificity or F1 Score. You are building a model to predict whether the next working day closing price will be above today’s closing or not.

When do you need high sensitivity and specificity?

Generally when building a classifier to predict the presence of a disease your aim is high sensitivity no matter the specificity. Identifying all subjects with the disease is more important than correctly predicting healthy as helthy.