Why ROC curve is not good for Imbalanced Data?

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 ROC curve good for Imbalanced Data?

ROC curves are appropriate when the observations are balanced between each class, whereas precision-recall curves are appropriate for imbalanced datasets. In both cases the area under the curve (AUC) can be used as a summary of the model performance.

Is ROC 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 precision recall curve better for Imbalanced Data?

FPR is considered better when it’s smaller since it indicates fewer false positives. In imbalanced data, the FPR tends to stay at small values due to the large numbers of negatives (i.e. making the denominator large). Thus, FPR becomes less informative for the model performance in this situation.

What is an acceptable AUC?

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 ROC curve with imbalanced data mean?

A ROC curve (blue) represents the performance of a classifier with the poor early retrieval level for the balanced case. A point (red circle) is selected for comparison. The same point for the imbalanced case also represents specificity 0.84 and sensitivity 0.5.

How does Croc work with imbalanced datasets?

Two CROC plots show the same curves despite of different positive and negative ratios. Both plots have five curves with different performance levels under balanced and imbalanced scenarios. Accordingly, all AUC (CROC) scores are unchanged between balanced and imbalanced datasets.

How can such classifier be represented in a ROC curve plot?

In a ROC plot the diagonal is the result that would be obtained from a random classifier. In case of an unbalanced output y the performance of a random classifier can be improved choosing 0 or 1 with different probabilities. How can the performance of such classifier be represented in a ROC curve plot?

How are precision recall curves and ROC curves related?

The Precision-Recall AUC is just like the ROC AUC, in that it summarizes the curve with a range of threshold values as a single score. The score can then be used as a point of comparison between different models on a binary classification problem where a score of 1.0 represents a model with perfect skill.

Why ROC Curve is not good for Imbalanced Data?

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.

When would you choose to evaluate with a PR curve versus a ROC Curve?

The key difference is that ROC curves will be the same no matter what the baseline probability is, but PR curves may be more useful in practice for needle-in-haystack type problems or problems where the “positive” class is more interesting than the negative class.

Is AUC good for imbalanced datasets?

ROC curves are appropriate when the observations are balanced between each class, whereas precision-recall curves are appropriate for imbalanced datasets. In both cases the area under the curve (AUC) can be used as a summary of the model performance.

Which is better ROC or AUC for imbalanced data?

If two ROCs cross, the ROC with the higher AUC will have at least a measurable subset of thresholds where ROC with inferior AUC is a better classifier. What about imbalanced data? So if I have 95 data points of class 1 and 5 of class 2 and my classifier always predicts class 1, i would still have a accuracy of 95%.

What is the difference between ROC AUC and PR AUC?

Similarly to ROC AUC score you can calculate the Area Under the Precision-Recall Curve to get one number that describes model performance. You can also think of PR AUC as the average of precision scores calculated for each recall threshold.

When is AUC misleading due to imbalanced data?

By random chance, your AUC can be misleadingly high, or misleadingly low with equal probability if you predict all ones with a small sample of ones. One way in which AUC can be misleadingly high is if you have imbalanced data in favor of 0 s, and a model with high recall but low precision, as I explained here.

When does precision start to fall on ROC AUC?

For the positive class precision is starting to fall as soon as we are recalling 0.2 of true positives and by the time we hit 0.8, it decreases to around 0.7. Similarly to ROC AUC score you can calculate the Area Under the Precision-Recall Curve to get one number that describes model performance.