Сравнение на методи

	Точност	Матрица на объркването	F1-резултат
Област	Оценка на модели	Оценка на модели	Оценка на модели
Семейство	MCDM	MCDM	MCDM
Година на възникване	20th century	20th century	1979
Създател	Historical statistical foundations	Statistical foundations	C. J. van Rijsbergen
Тип	Evaluation metric	Evaluation visualization	Evaluation metric
Основополагащ източник	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Други названия	Overall Accuracy, Correct Classification Rate	Error Matrix, Contingency Table	F-measure, Harmonic Mean
Свързани	5	5	5
Резюме	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 confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.	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.