Linganisha mbinu

	Usahihi	Log-Loss (aucdoti ya msalaba-entropi)
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM
Mwaka wa asili	20th century	1990s
Mwanzilishi	Historical statistical foundations	Information theory and machine learning literature
Aina	Evaluation metric	Loss function
Chanzo asilia	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.
Majina mbadala	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
Zinazohusiana	5	3
Muhtasari	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.