Can AUC higher than accuracy?

Can 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.

Does class imbalance affect AUC?

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.

How can it be that the AUC is large while the accuracy is low at the same time?

So how can it be that the AUC is large while the accuracy is low at the same time? Well this may happen if your classifier achieves the good performance on the positive class (high AUC) at the cost of a high false negatives rate (or a low number of true negative).

Why is AUC high?

3 Answers. One possible reason you can get high AUROC with what some might consider a mediocre prediction is if you have imbalanced data (in favor of the “zero” prediction), high recall, and low precision.

What does an AUC of 0.9 mean?

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.

Why is AUC bad for imbalanced datasets?

Although widely used, the ROC AUC is not without problems. For imbalanced classification with a severe skew and few examples of the minority class, the ROC AUC can be misleading. This is because a small number of correct or incorrect predictions can result in a large change in the ROC Curve or ROC AUC score.

Is AUC 0.9 good?

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 AUC 0.5 mean?

When AUC=0.5, then the classifier is not able to distinguish between Positive and Negative class points. Meaning either the classifier is predicting random class or constant class for all the data points.

Why is AUC higher for classifier that is less accurate?

Although it has a slightly lower accuracy, note that its sensitivity is much higher at this cut-off… Finally, you cannot compare the accuracy (a performance at one threshold) with the AUC (an average performance on all possible thresholds). As these metrics measure different things, it is not surprising that they are different.

Can a AUC be generalized to a multi class setting?

The AUC can also be generalized to the multi-class setting. As discussed in this Stack Exchange thread, we can visualize the performance of a multi-class model by plotting the performance of K binary classifiers.

What’s the difference between ROC and AUC for accuracy?

Accuracy is computed at the threshold value of 0.5. While AUC is computed by adding all the “accuracies” computed for all the possible threshold values. ROC can be seen as an average (expected value) of those accuracies when are computed for all threshold values. So, how do i really judge/compare the classification performances of A and B?

How are AUC and accuracy used in machine learning?

For a given choice of threshold, you can compute accuracy, which is the proportion of true positives and negatives in the whole data set. AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else.