Bandingkan metode

	AUC Presisi-Recall	Akurasi	Recall (Sensitivitas)
Bidang	Evaluasi Model	Evaluasi Model	Evaluasi Model
Keluarga	MCDM	MCDM	MCDM
Tahun asal	2006	20th century	20th century
Pencetus	Davis and Goadrich	Historical statistical foundations	Historical statistical foundations
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.	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	Sensitivity, True Positive Rate, TPR
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.	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.