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Ketepatan (Specificity)×Skor F1×Koefisien Korelasi Matthews×
BidangPenilaian ModelPenilaian ModelPenilaian Model
KeluargaMCDMMCDMMCDM
Tahun asal20th century19791975
PengasasHistorical statistical foundationsC. J. van RijsbergenBrian W. Matthews
JenisEvaluation metricEvaluation metricEvaluation metric
Sumber perintisFawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗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. DOI ↗
AliasTrue Negative Rate, TNRF-measure, Harmonic MeanPhi Coefficient, Binary Classification Correlation
Berkaitan555
RingkasanSpecificity measures the proportion of actual negative cases that were correctly identified as negative by the classifier. It answers the question: 'Of all the cases that were truly negative, how many did we correctly reject?' Specificity is complementary to recall and is essential when false positives are 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.
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ScholarGateBandingkan kaedah: Specificity · F1-Score · Matthews Correlation Coefficient. Dicapai 2026-06-18 daripada https://scholargate.app/ms/compare