手法を比較

	精度	Log-Loss（交差エントロピー損失）
分野	モデル評価	モデル評価
系統	MCDM	MCDM
提唱年	20th century	1990s
提唱者	Historical statistical foundations	Information theory and machine learning literature
種類	Evaluation metric	Loss function
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.
別名	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
関連	5	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.