What is a bad AUC score?

What is a bad AUC score?

Statistical Analysis 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 AUC score is good?

0.7 to 0.8
AUC can be computed using the trapezoidal rule. 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 does a low F1 score mean?

An F1 score reaches its best value at 1 and worst value at 0. A low F1 score is an indication of both poor precision and poor recall.

What AUC score means?

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. AUC is scale-invariant. It measures how well predictions are ranked, rather than their absolute values.

Is AUC a good performance measure?

AUC is better measure of classifier performance than accuracy because it does not bias on size of test or evaluation data. Accuracy is always biased on size of test data. In most of the cases, we use 20\% data as evaluation or test data for our algorithm of total training data.

What is the difference between AUC and Roc?

AUC – ROC curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. An excellent model has AUC near to the 1 which means it has good measure of separability.

What does it mean when AUC is low?

A low AUC might say that you are not using the best metric for the problem at hand. It could also mean overfitting [ 5] but this is hard to tell if you don’t specify on which type of dataset you are getting this low value. Is it in your training, cross-validation, or test set?

What is AUC – ROC curve in machine learning?

In Machine Learning, performance measurement is an essential task. So when it comes to a classification problem, we can count on an AUC – ROC Curve. When we need to check or visualize the performance of the multi-class classification problem, we use the AUC (Area Under The Curve) ROC (Receiver Operating Characteristics) curve.

What is the difference between AUC and RMSE?

AUC is a metric for classification and RMSE is a metric for regression. AUC [ 1] is a metric used (mostly) for (binary) classification [ 2]. RMSE [ 3] is a metric used (mostly) for regression [ 4]. A low AUC might say that you are not using the best metric for the problem at hand.