has part denotes graph x-axis y-axis true positive rate precision precision-recall curve PR-curve adapted from ChatGPT using the prompt: "What is the difference between ROC curve and PR curve" Philippe Rocca-Serra The precision-recall curve is a graph which plots the sensitivity or recall on the X-axis and the precision on the Y-axis. The precision-recall curves focuses on the trade-off between Precision and Recall at various thresholds. It also highlights the performance of the model for the positive class (often the minority or more important class). It is particularly suited for for imbalanced datasets where the positive class is rare. The PR-curve is more sensitive to class imbalance. As precision drops significantly if the model predicts too many false positives, it makesthe PR-curve better for evaluating performance on imbalanced datasets. ready for release