So sánh phương pháp

	Độ chính xác cân bằng	Ma trận nhầm lẫn	Điểm F1	Độ chính xác (Precision)
Lĩnh vực	Đánh giá mô hình	Đánh giá mô hình	Đánh giá mô hình	Đánh giá mô hình
Họ	MCDM	MCDM	MCDM	MCDM
Năm ra đời	2010	20th century	1979	20th century
Người khởi xướng	Brodersen, Ong, Stephan, and Buhmann	Statistical foundations	C. J. van Rijsbergen	Historical statistical foundations
Loại	Evaluation metric	Evaluation visualization	Evaluation metric	Evaluation metric
Công trình gốc	Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. 20th International Conference on Pattern Recognition (ICPR), 3121-3124.	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Tên gọi khác	Average Recall, Equal-weight Average Sensitivity	Error Matrix, Contingency Table	F-measure, Harmonic Mean	Positive Predictive Value, PPV
Liên quan	5	5	5	5
Tóm tắt	Balanced accuracy is the average of recall values computed for each class separately. It corrects for class imbalance by giving equal weight to the performance on each class, regardless of class frequency in the dataset.	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.	Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.