What is a good AUPR?

What is a good AUPR?

The baseline of AUPRC is equal to the fraction of positives. If a dataset consists of 8% cancer examples and 92% healthy examples, the baseline AUPRC is 0.08, so obtaining an AUPRC of 0.40 in this scenario is good!

What is a good ROC value?

AREA UNDER THE ROC 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 value?

However, there is definitely value in understanding that a 0.95 AUC-ROC, for example, means that you have essentially solved the problem and have a very, very good classifier. Whereas an AUC of 0.6 for finding profitable investments might be, strictly speaking, better than random, but not much better.

What does the AUC score tell you?

The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.

Is AUC better than f1 score?

F1 score is applicable for any particular point on the ROC curve. You may think of it as a measure of precision and recall at a particular threshold value whereas AUC is the area under the ROC curve. For F score to be high, both precision and recall should be high.

Is AUC 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.

What does AUC of 0.5 mean?

A perfect predictor gives an AUC-ROC score of 1, a predictor which makes random guesses has an AUC-ROC score of 0.5. If you get a score of 0 that means the classifier is perfectly incorrect, it is predicting the incorrect choice 100% of the time.

Is AUC a percentage?

AUC represents the probability that a random positive (green) example is positioned to the right of a random negative (red) example. AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.

What does AUC less than 0.5 mean?

Usually, the AUC is in the range [0.5,1] because useful classifiers should perform better than random. In principle, however, the AUC can also be smaller than 0.5, which indicates that a classifier performs worse than a random classifier.

Why is ROC not accurate?

Accuracy vs ROC AUC The first big difference is that you calculate accuracy on the predicted classes while you calculate ROC AUC on predicted scores. That means you will have to find the optimal threshold for your problem. Moreover, accuracy looks at fractions of correctly assigned positive and negative classes.

What does a good ROC curve look like?

Generally, tests are categorized based on the area under the ROC curve. The closer an ROC curve is to the upper left corner, the more efficient is the test. In FIG. XIII test A is superior to test B because at all cut-offs the true positive rate is higher and the false positive rate is lower than for test B.

Is AUC 0.8 good?

What does AUC of 0.6 mean?

In general, the rule of thumb for interpreting AUC value is: AUC=0.5. No discrimination, e.g., randomly flip a coin. 0.6≥AUC>0.5. Poor discrimination.