Linganisha mbinu

	Umahiri (Specificity)	F1-Score	Kiwango cha Uwiano cha Matthews	Usahihi
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM	MCDM	MCDM
Mwaka wa asili	20th century	1979	1975	20th century
Mwanzilishi	Historical statistical foundations	C. J. van Rijsbergen	Brian W. Matthews	Historical statistical foundations
Aina	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Chanzo asilia	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.
Majina mbadala	True Negative Rate, TNR	F-measure, Harmonic Mean	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV
Zinazohusiana	5	5	5	5
Muhtasari	Specificity 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.	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.