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Is AUC affected by class imbalance?
The ROC AUC is sensitive to class imbalance in the sense that when there is a minority class, you typically define this as the positive class and it will have a strong impact on the AUC value.
Why is high AUC better?
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 higher than accuracy?
Why is AUC higher for a classifier that is less accurate than for one that is more accurate? In terms of accuracy and other measures, A performs comparatively worse than B. However, when I use the R packages ROCR and AUC to perform ROC analysis, it turns out that the AUC for A is higher than the AUC for B.
Why AUC is a good metric?
The AUC is an estimate of the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance. For this reason, the AUC is widely thought to be a better measure than a classification error rate based upon a single prior probability or KS statistic threshold.
What is a bad AUC?
The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier.
When is ROC AUC sensitive to class imbalance?
It depends how you mean the word sensitive. The ROC AUC is sensitive to class imbalance in the sense that when there is a minority class, you typically define this as the positive class and it will have a strong impact on the AUC value. This is very much desirable behaviour.
How does AUC tell you your model’s performance?
AUC tells you your model’s performance pretty much, while addressing the issue of class imbalance. To be scientifically safe, I’d rather say it is insensitive to changes in class distribution. For example, and to make this as simple as possible, let’s take a look at a binary classification problem where the positive class is dominant.
Which is better, high AUC or low AUC?
Classifier A may produce a higher AUC than B, while B performs better for a majority of the thresholds with which you may actually use the classifier. And in fact empirical studies have shown that it is indeed very common for ROC curves of common classifiers to cross.
What does it mean when AUC does not change?
I think it means that AUC is insensitive to imbalance in test data, rather than imbalance in training data. In other words, only changing the distribution of positive and negative classes in the test data, the AUC value may not change much. But if we change the distribution in the training data, the AUC value may largely change.