Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Ketepatan× | Akurasi Seimbang× | |
|---|---|---|
| Bidang | Penilaian Model | Penilaian Model |
| Keluarga | MCDM | MCDM |
| Tahun asal≠ | 20th century | 2010 |
| Pengasas≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann |
| Jenis | Evaluation metric | Evaluation metric |
| Sumber perintis≠ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ | 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. DOI ↗ |
| Alias | Overall Accuracy, Correct Classification Rate | Average Recall, Equal-weight Average Sensitivity |
| Berkaitan | 5 | 5 |
| Ringkasan≠ | 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. | 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. |
| ScholarGateSet data ↗ |
|
|