Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Akurasi Seimbang× | Matriks Kebingungan× | Skor F1× | |
|---|---|---|---|
| Bidang | Penilaian Model | Penilaian Model | Penilaian Model |
| Keluarga | MCDM | MCDM | MCDM |
| Tahun asal≠ | 2010 | 20th century | 1979 |
| Pengasas≠ | Brodersen, Ong, Stephan, and Buhmann | Statistical foundations | C. J. van Rijsbergen |
| Jenis≠ | Evaluation metric | Evaluation visualization | Evaluation metric |
| Sumber perintis≠ | 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 ↗ | Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC. link ↗ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ |
| Alias | Average Recall, Equal-weight Average Sensitivity | Error Matrix, Contingency Table | F-measure, Harmonic Mean |
| Berkaitan | 5 | 5 | 5 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
|
|
|