What is precision and recall and how do they relate to the ROC curve?

It is only concerned with the correct prediction of the minority class, class 1. A precision-recall curve is a plot of the precision (y-axis) and the recall (x-axis) for different thresholds, much like the ROC curve.

What is the difference between the ROC curve and the precision-recall 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.

What does the precision-recall curve mean?

The precision-recall curve shows the tradeoff between precision and recall for different threshold. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.

How is true positive rate and recall related?

Recall and True Positive Rate (TPR) are exactly the same. So the difference is in the precision and the false positive rate. The main difference between these two types of metrics is that precision denominator contains the False positives while false positive rate denominator contains the true negatives.

What can you say about the precision recall PR curve?

PR curve has the Recall value (TPR) on the x-axis, and precision = TP/(TP+FP) on the y-axis. Precision helps highlight how relevant the retrieved results are, which is more important while judging an IR system. Hence, a PR curve is often more common around problems involving information retrieval.

Why do we use Precision and Recall?

You may decide to use precision or recall on your imbalanced classification problem. Maximizing precision will minimize the number false positives, whereas maximizing the recall will minimize the number of false negatives.

What is recall and precision in information retrieval?

Precision = Total number of documents retrieved that are relevant/Total number of documents that are retrieved. Recall = Total number of documents retrieved that are relevant/Total number of relevant documents in the database.