In Machine Learning, PR AUC (area under the PR curve) is a metric used to measure the performance of binary classifiers. PR AUC is the area under the Precision-Recall curve, which is a graph that shows the tradeoff between the precision and recall of a binary classifier.
Before diving deep into PR AUC, let’s first understand Precision and Recall, these are two metrics that are often used in binary classification problems.
Precision:
Precision is a measure of the accuracy of a classifier, indicating the fraction of true positives among the samples identified as positive.
Recall:
Recall is a measure of the completeness of a classifier, indicating the fraction of true positives that were correctly identified as positive.
To get a better understanding of PR AUC, let’s take an example of a spam classification problem. Suppose a classifier is trained to identify spam emails. For any given email, the classifier can either classify it as spam or not spam (ham).
If the classifier is highly precise, it means that it identified most of the emails classified as spam, as actually spam. However, there still might be some emails that are incorrectly classified as spam. Therefore, Precision is a measure of the fraction of true positives among the samples identified as positive.
On the other hand, recall measures the fraction of true positives that were correctly identified as positive. In other words, if the classifier has high recall, it means that it identified most spam emails, but it might also identify some emails as spam that are not actually spam.
Now we come to PR AUC. The Precision-Recall curve shows the tradeoff between precision and recall for different thresholds. A threshold is a value that separates the decision boundary between positive and negative for a given classifier. For instance, if the threshold for spam classification is lower, it means that the classifier is likely to classify more emails as spam, which may result in increased recall but lower precision.
The PR AUC is the area under the Precision-Recall curve, which quantifies the overall performance of the classifier, taking into account both precision and recall. A perfect classifier would have a value of 1, while a value closer to 0.5 indicates random classification.
In conclusion, PR AUC is a metric used to measure the performance of binary classifiers, taking into account both precision and recall. It helps to evaluate the classifier’s performance for different thresholds and is a better metric than accuracy in cases of imbalanced data or when the cost of false positives and false negatives is not the same.