Description: The Precision-Recall Curve is a graphical representation that illustrates the relationship between precision and recall of a classification model based on different decision thresholds. Precision refers to the proportion of true positives over the total number of predicted positive instances, while recall (or sensitivity) is the proportion of true positives over the total number of actual positive instances. This curve allows visualization of how these two metrics vary as the decision threshold of the model is adjusted, providing a valuable tool for evaluating its performance. Generally, as the threshold increases, precision tends to increase, but recall decreases, and vice versa. The Precision-Recall Curve is particularly useful in contexts where classes are imbalanced, as it offers a clearer view of the model’s performance on the class of interest, rather than relying solely on overall accuracy. This representation is fundamental in the field of machine learning and data mining, as it allows researchers and developers to select the optimal threshold that maximizes model performance according to their specific needs.
