השוואת שיטות

	דיוק	שגיאה מוחלטת ממוצעת (MAE)
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	20th century	1799
הוגה השיטה	Historical statistical foundations	Pierre-Simon Laplace
סוג	Evaluation metric	Robust distance-based metric
מקור מכונן	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
כינויים	Overall Accuracy, Correct Classification Rate	MAE, L1 error, mean absolute deviation
קשורות	5	3
תקציר	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.	Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.