পদ্ধতির তুলনা করুন

	বিভ্রান্তি ম্যাট্রিক্স	Accuracy (সঠিকতা)	Recall (Sensitivity)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM	MCDM
উদ্ভবের বছর	20th century	20th century	20th century
প্রবর্তক	Statistical foundations	Historical statistical foundations	Historical statistical foundations
ধরন	Evaluation visualization	Evaluation metric	Evaluation metric
মৌলিক উৎস	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
অপর নাম	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
সম্পর্কিত	5	5	5
সারসংক্ষেপ	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.	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.	Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.