手法を比較

	精度	Log-Loss（交差エントロピー損失）	平均絶対誤差 (MAE)
分野	モデル評価	モデル評価	モデル評価
系統	MCDM	MCDM	MCDM
提唱年	20th century	1990s	1799
提唱者	Historical statistical foundations	Information theory and machine learning literature	Pierre-Simon Laplace
種類	Evaluation metric	Loss function	Robust distance-based metric
原典	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
別名	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss	MAE, L1 error, mean absolute deviation
関連	5	3	3
概要	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.	Log-loss measures the difference between predicted probabilities and actual labels, penalizing confident wrong predictions more than uncertain ones. It is a standard loss function in machine learning optimization and evaluates probabilistic classifier calibration.	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.