পদ্ধতির তুলনা করুন

	লগ-লস (ক্রস-এন্ট্রপি লস)	Accuracy (সঠিকতা)	F1-স্কোর
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM	MCDM
উদ্ভবের বছর	1990s	20th century	1979
প্রবর্তক	Information theory and machine learning literature	Historical statistical foundations	C. J. van Rijsbergen
ধরন	Loss function	Evaluation metric	Evaluation metric
মৌলিক উৎস	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.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
অপর নাম	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean
সম্পর্কিত	3	5	5
সারসংক্ষেপ	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.	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.