What is area under the PR curve

The area under the precision-recall (PR) curve is an important metric in machine learning. It is used to evaluate the performance of a model on a binary classification task. The PR curve is a graph that plots the precision (the fraction of true positives over all positives) against the recall (the fraction of true positives over all actual positives). The area under the PR curve (AUPRC) is the area under this curve, which is used as a measure of the model’s performance.

The AUPRC is a good metric to use when the class distribution is imbalanced, meaning that one class is much more common than the other. In this case, accuracy is not a good measure of performance because it does not take into account the class imbalance. The AUPRC, on the other hand, is a better measure of performance because it takes into account the class imbalance.

The AUPRC is also useful for evaluating models that are designed to detect rare events. In this case, accuracy is not a good measure of performance because it does not take into account the rarity of the event. The AUPRC is better because it takes into account the rarity of the event.

The AUPRC is a good metric for evaluating models in a variety of situations. It is especially useful for evaluating models on imbalanced datasets and for detecting rare events. It is important to note that the AUPRC is not the only metric that should be used to evaluate a model. Other metrics, such as the receiver operating characteristic (ROC) curve, should also be considered.