Bandingkan metode

	Akurasi	Log-Loss (Cross-Entropy Loss)
Bidang	Evaluasi Model	Evaluasi Model
Keluarga	MCDM	MCDM
Tahun asal	20th century	1990s
Pencetus	Historical statistical foundations	Information theory and machine learning literature
Tipe	Evaluation metric	Loss function
Sumber perintis	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.
Alias	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
Terkait	5	3
Ringkasan	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.