Bandingkan metode

	AUC Presisi-Recall	Akurasi	Skor-F1
Bidang	Evaluasi Model	Evaluasi Model	Evaluasi Model
Keluarga	MCDM	MCDM	MCDM
Tahun asal	2006	20th century	1979
Pencetus	Davis and Goadrich	Historical statistical foundations	C. J. van Rijsbergen
Tipe	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.
Alias	PR AUC, PR Curve	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean
Terkait	4	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.