পদ্ধতির তুলনা করুন

	Recall (Sensitivity)	F1-স্কোর	ম্যাথিউস কোরিলেশন কোএফিসিয়েন্ট (MCC)	নির্ভুলতা
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM	MCDM	MCDM
উদ্ভবের বছর	20th century	1979	1975	20th century
প্রবর্তক	Historical statistical foundations	C. J. van Rijsbergen	Brian W. Matthews	Historical statistical foundations
ধরন	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
মৌলিক উৎস	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.	Matthews, B. W. (1975). Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
অপর নাম	Sensitivity, True Positive Rate, TPR	F-measure, Harmonic Mean	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV
সম্পর্কিত	5	5	5	5
সারসংক্ষেপ	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.	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.	The Matthews Correlation Coefficient (MCC) is a correlation measure between predicted and actual binary classifications. It ranges from -1 to 1 and is considered one of the most reliable single-score metrics for evaluating binary classifiers, especially on imbalanced datasets.	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.