You can use this plot to make an educated decision when it comes to the classic precision/recall dilemma. Obviously, the higher the recall, the lower the precision. Knowing at which recall your precision starts to fall fast can help you choose the threshold and deliver a better model.
https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc
The precision recall curve is a handy plot to showcase the relationship and tradeoff between precision recall values as we adjust the decision threshold of the classifier. What is the decision threshold? The decision threshold, also called the classification threshold, is a cutoff point used in binary classification to convert the probability score output by a machine learning model into a final class prediction (positive or negative). Most binary classification models (like logistic regression) output a probability between 0 and 1 that an instance belongs to the positive class. The decision threshold determines which probability values map to which class: If the predicted probability is greater than or equal to the threshold, the instance is classified as the positive class. If the predicted probability is less than the threshold, the instance is classified as the negative class. How it Works By default, the threshold is often set to 0.5. A probability of \ge 0.5 \rightarrow Positive Class A probability of < 0.5 \rightarrow Negative Class However, this default isn't always optimal. The threshold is a hyperparameter that can be tuned to balance the trade-off between precision and recall, which is what the precision-recall curve helps to visualize. Threshold and Precision/Recall Trade-off Adjusting the decision threshold directly impacts the number of false positives (FP) and false negatives (FN), which in turn changes the precision and recall values.
A higher AUC-PR value signifies better performance, with a maximum value of 1 indicating perfect precision and recall trade-off. https://www.superannotate.com/blog/mean-average-precision-and-its-uses-in-object-detection
