השוואת שיטות

	Log-Loss (Cross-Entropy Loss)	מדד F1
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	1990s	1979
הוגה השיטה	Information theory and machine learning literature	C. J. van Rijsbergen
סוג	Loss function	Evaluation metric
מקור מכונן	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
כינויים	Cross-Entropy Loss, Logloss	F-measure, Harmonic Mean
קשורות	3	5
תקציר	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.