পদ্ধতির তুলনা করুন

	প্রিসিশন-রিকল এউসি (Precision-Recall AUC)	Accuracy (সঠিকতা)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM
উদ্ভবের বছর	2006	20th century
প্রবর্তক	Davis and Goadrich	Historical statistical foundations
ধরন	Evaluation metric	Evaluation metric
মৌলিক উৎস	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.
অপর নাম	PR AUC, PR Curve	Overall Accuracy, Correct Classification Rate
সম্পর্কিত	4	5
সারসংক্ষেপ	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.