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Is area under curve accurate?
The AUC is an overall summary of diagnostic accuracy. AUC equals 0.5 when the ROC curve corresponds to random chance and 1.0 for perfect accuracy. On rare occasions, the estimated AUC is <0.5, indicating that the test does worse than chance.
What is area under PR curve?
Abstract. The area under the precision-recall curve (AUCPR) is a single number summary of the information in the precision-recall (PR) curve. Similar to the receiver operating characteristic curve, the PR curve has its own unique properties that make estimating its enclosed area challenging.
What is a good area under the curve?
In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.
What is a good AUC PR score?
The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier.
What makes a PR curve a good curve?
A good PR curve has greater AUC (area under curve). In the figure above, the classifier corresponding to the blue line has better performance than the classifier corresponding to the green line. It is important to note that the classifier that has a higher AUC on the ROC curve will always have a higher AUC on the PR curve as well.
What’s the difference between ROC and area under PR?
Figure 3 contains ROC and PR curves for identical models, clearly showing the difference between the two. To compare area under the PR versus area under ROC you can compare tables 1-2 (AUPR) and tables 3-4 (AUROC) where you can see that AUPR shows much larger differences between individual models than AUROC.
How to compare area under the ROC curve?
To compare area under the PR versus area under ROC you can compare tables 1-2 (AUPR) and tables 3-4 (AUROC) where you can see that AUPR shows much larger differences between individual models than AUROC. This emphasizes the suitability of PR curves once more.
When to use difference in area under curve ( AUC )?
Difference in area under curve (AUC) The difference in areas under the ROC curves compares two or more diagnostic tests. It is imperative when comparing tests that you choose the correct type of analysis dependent on how you collect the data. If the tests are performed on the same subjects (paired design) the test results are usually correlated.