Does AUC work for Imbalanced Data?

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Does AUC work 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.

What does high AUC mean?

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.

Why is precision recall curve better for Imbalanced Data?

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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 AUC and ROC in machine learning?

Why we use AUC ROC curve?

What is AUC (area under the curve) 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. It is one of the most important evaluation metrics for checking any classification model’s performance.

What is the difference between ROC and AUC?

The Receiver Operating Characteristic (ROC) curve is a probability curve that illustrates how good our binary classification is in classifying classes based on true-positive and false-positive rates. The Area Under Curve (AUC) is a metric that ranges from 0 to 1. It is the area under the (ROC) curve. An example of an ROC curve and AUC.

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What is an ROC curve in machine learning?

Estimated Time: 8 minutes. An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds.

How does AUC affect the performance of the model?

The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes. When AUC = 1, then the classifier is able to perfectly distinguish between all the Positive and the Negative class points correctly.