Порівняння методів

	Точність	F1-Score	Коефіцієнт кореляції Метьюза	Чутливість (Recall)
Галузь	Оцінювання моделей	Оцінювання моделей	Оцінювання моделей	Оцінювання моделей
Родина	MCDM	MCDM	MCDM	MCDM
Рік появи	20th century	1979	1975	20th century
Автор методу	Historical statistical foundations	C. J. van Rijsbergen	Brian W. Matthews	Historical statistical foundations
Тип	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Основоположне джерело	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.
Інші назви	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean	Phi Coefficient, Binary Classification Correlation	Sensitivity, True Positive Rate, TPR
Пов'язані	5	5	5	5
Підсумок	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	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.	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.