เปรียบเทียบวิธี

	สัมประสิทธิ์สหสัมพันธ์ของแมทธิวส์	คะแนน F1	ความแม่นยำ (Precision)	การเรียกคืน (ความไว)
สาขาวิชา	การประเมินแบบจำลอง	การประเมินแบบจำลอง	การประเมินแบบจำลอง	การประเมินแบบจำลอง
ตระกูล	MCDM	MCDM	MCDM	MCDM
ปีกำเนิด	1975	1979	20th century	20th century
ผู้ริเริ่ม	Brian W. Matthews	C. J. van Rijsbergen	Historical statistical foundations	Historical statistical foundations
ประเภท	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
แหล่งต้นตำรับ	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.	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
ชื่อเรียกอื่น	Phi Coefficient, Binary Classification Correlation	F-measure, Harmonic Mean	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
ที่เกี่ยวข้อง	5	5	5	5
สรุป	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.	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.	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.	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.