Vertaile menetelmiä

	Tarkkuus	Log-Loss (ristientropiahäviö)
Tieteenala	Mallien arviointi	Mallien arviointi
Menetelmäperhe	MCDM	MCDM
Syntyvuosi	20th century	1990s
Kehittäjä	Historical statistical foundations	Information theory and machine learning literature
Tyyppi	Evaluation metric	Loss function
Alkuperäislähde	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.
Rinnakkaisnimet	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
Liittyvät	5	3
Tiivistelmä	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.