Σύγκριση μεθόδων

	Απώλεια λογαρίθμου (Απώλεια διασταυρούμενης εντροπίας)	Βαθμολογία F1
Πεδίο	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων
Οικογένεια	MCDM	MCDM
Έτος προέλευσης	1990s	1979
Δημιουργός	Information theory and machine learning literature	C. J. van Rijsbergen
Τύπος	Loss function	Evaluation metric
Θεμελιώδης πηγή	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Εναλλακτικές ονομασίες	Cross-Entropy Loss, Logloss	F-measure, Harmonic Mean
Συναφείς	3	5
Σύνοψη	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.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.