A precision-recall curve is a graphical representation that illustrates the trade-off between precision and recall for different threshold values in a binary classification model. It helps evaluate the performance of a model, particularly when dealing with imbalanced datasets, by showing how many relevant instances are retrieved (recall) versus how many of those retrieved are actually relevant (precision). This curve is especially important for applications where false positives and false negatives carry different costs, such as in motion detection and tracking scenarios.
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