পদ্ধতির তুলনা করুন

	Accuracy (সঠিকতা)	বিভ্রান্তি ম্যাট্রিক্স	F1-স্কোর
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM	MCDM
উদ্ভবের বছর	20th century	20th century	1979
প্রবর্তক	Historical statistical foundations	Statistical foundations	C. J. van Rijsbergen
ধরন	Evaluation metric	Evaluation visualization	Evaluation metric
মৌলিক উৎস	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
অপর নাম	Overall Accuracy, Correct Classification Rate	Error Matrix, Contingency Table	F-measure, Harmonic Mean
সম্পর্কিত	5	5	5
সারসংক্ষেপ	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 confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.	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.