Vertaile menetelmiä

	Log-Loss (ristientropiahäviö)	Tarkkuus
Tieteenala	Mallien arviointi	Mallien arviointi
Menetelmäperhe	MCDM	MCDM
Syntyvuosi	1990s	20th century
Kehittäjä	Information theory and machine learning literature	Historical statistical foundations
Tyyppi	Loss function	Evaluation metric
Alkuperäislähde	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Rinnakkaisnimet	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate
Liittyvät	3	5
Tiivistelmä	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.	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.