Contents
Is AUC affected by prevalence?
AUC itself cannot account for how changing prevalence impacts on results for individual patients, so instead sensitivity and specificity are used, with the operating point directed by prevalence.
How do you read sensitivity and specificity?
Sensitivity is calculated based on how many people have the disease (not the whole population). It can be calculated using the equation: sensitivity=number of true positives/(number of true positives+number of false negatives). Specificity is calculated based on how many people do not have the disease.
Is an AUC of 0.6 good?
The area under the ROC curve (AUC) results were considered excellent for AUC values between 0.9-1, good for AUC values between 0.8-0.9, fair for AUC values between 0.7-0.8, poor for AUC values between 0.6-0.7 and failed for AUC values between 0.5-0.6.
Which is more important sensitivity or specificity?
The more sensitive a test, the less likely an individual with a negative test will have the disease and thus the greater the negative predictive value. The more specific the test, the less likely an individual with a positive test will be free from disease and the greater the positive predictive value.
What are the parameters of ROC and AUC?
Under the hood, these are very simple calculation parameters which just needs a little demystification. The concept of ROC and AUC builds upon the knowledge of Confusion Matrix, Specificity and Sensitivity.
Which is better sensitivity or 1 specificity in AUC?
Sensitivity is on Y-axis and (1-Specificity) is on X-axis. Higher the AUC score, better the model. Diagonal line represents random classification model.
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?
When is a pair discordant on the AUC?
A pair is discordant if 0 (observation without the desired outcome i.e. non-event) has a higher predicted probability than 1 (observation with the outcome i.e. event). A pair is tied if 1 (observation with the desired outcome i.e. event) has same predicted probability than 0 (observation without the outcome i.e. non-event).