পদ্ধতির তুলনা করুন

	ম্যাথিউস কোরিলেশন কোএফিসিয়েন্ট (MCC)	ভারসাম্যপূর্ণ নির্ভুলতা (Balanced Accuracy)	নির্ভুলতা
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM	MCDM
উদ্ভবের বছর	1975	2010	20th century
প্রবর্তক	Brian W. Matthews	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations
ধরন	Evaluation metric	Evaluation metric	Evaluation metric
মৌলিক উৎস	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.	Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. 20th International Conference on Pattern Recognition (ICPR), 3121-3124.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
অপর নাম	Phi Coefficient, Binary Classification Correlation	Average Recall, Equal-weight Average Sensitivity	Positive Predictive Value, PPV
সম্পর্কিত	5	5	5
সারসংক্ষেপ	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.	Balanced accuracy is the average of recall values computed for each class separately. It corrects for class imbalance by giving equal weight to the performance on each class, regardless of class frequency in the dataset.	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.