Bandingkan kaedah

	AUC Precision-Recall	Ketepatan	Skor F1	Kepersisan	Deria (Sensitiviti)
Bidang	Penilaian Model	Penilaian Model	Penilaian Model	Penilaian Model	Penilaian Model
Keluarga	MCDM	MCDM	MCDM	MCDM	MCDM
Tahun asal	2006	20th century	1979	20th century	20th century
Pengasas	Davis and Goadrich	Historical statistical foundations	C. J. van Rijsbergen	Historical statistical foundations	Historical statistical foundations
Jenis	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Sumber perintis	Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 233-240.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	PR AUC, PR Curve	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
Berkaitan	4	5	5	5	5
Ringkasan	The Precision-Recall Area Under the Curve (PR AUC) is the area under the curve formed by plotting recall on the x-axis and precision on the y-axis. It is particularly useful for evaluating classifiers on imbalanced datasets, where it is often more informative than ROC AUC.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.	Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.	Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.