পদ্ধতির তুলনা করুন

	প্রিসিশন-রিকল এউসি (Precision-Recall AUC)	F1-স্কোর	Recall (Sensitivity)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM	MCDM
উদ্ভবের বছর	2006	1979	20th century
প্রবর্তক	Davis and Goadrich	C. J. van Rijsbergen	Historical statistical foundations
ধরন	Evaluation metric	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.	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.
অপর নাম	PR AUC, PR Curve	F-measure, Harmonic Mean	Sensitivity, True Positive Rate, TPR
সম্পর্কিত	4	5	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.	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.	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.