What is Precision Recall Curve?
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A Precision-Recall curve is a graphical representation used to evaluate the performance of algorithms, particularly in object detection and classification tasks. Unlike traditional curves, statistical Precision-Recall curves ensure a monotonically non-increasing pattern, aiding in a more reliable assessment of algorithm quality. These curves are crucial in scenarios with imbalanced data distributions, common in real-world applications like disease diagnosis and fraud detection. The area under the Precision-Recall curve, known as Average Precision (AP), is a standard metric for comparing algorithm quality, calculated based on Statistical Precision and Statistical Recall values. This statistical approach enhances the assessment of algorithm performance, especially on small test datasets, by providing a more accurate and consistent evaluation.
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| Precision-Recall Curve is explored for clustering validation, offering a valuable metric in the presence of cluster imbalance, enhancing evaluation frameworks for both supervised and unsupervised learning models. | |
Open access•Posted Content | Precision-Recall Curve (PRC) is utilized in the proposed PRC classification tree for imbalanced data classification, focusing on maximizing the area under the curve and F-measure for variable selection. |
| Precision-Recall Curve (PRC) is utilized in the proposed algorithm for variable selection in imbalanced data classification, focusing on maximizing the area under the curve and F-measure for threshold selection. | |
29 Dec 2020 1 Citations | Precision-Recall curve assesses algorithm quality by plotting Precision against Recall. This paper introduces Statistical Precision-Recall curves for object detection evaluation, ensuring monotonically non-increasing curves for better comparison. |
01 Jan 2021 1 Citations | Precision-Recall curves measure object detection algorithm quality. Statistical approach ensures monotonous non-increasing curves, with lower average accuracy on small datasets, leveling out on larger sets. |
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