Comparer des méthodes

	Précision	Exactitude	Score F1	Coefficient de corrélation de Matthews
Domaine	Évaluation de modèles	Évaluation de modèles	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM	MCDM	MCDM
Année d'origine	20th century	20th century	1979	1975
Auteur d'origine	Historical statistical foundations	Historical statistical foundations	C. J. van Rijsbergen	Brian W. Matthews
Type	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Source fondatrice	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.	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.
Alias	Positive Predictive Value, PPV	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean	Phi Coefficient, Binary Classification Correlation
Apparentées	5	5	5	5
Résumé	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.	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.