Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Skor-F1× | Kerugian Hamming× | |
|---|---|---|
| Bidang | Evaluasi Model | Evaluasi Model |
| Keluarga | MCDM | MCDM |
| Tahun asal≠ | 1979 | 2000s |
| Pencetus≠ | C. J. van Rijsbergen | Information theory and multi-label learning |
| Tipe≠ | Evaluation metric | Loss function |
| Sumber perintis≠ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ | Schapire, R. E., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3), 135-168. DOI ↗ |
| Alias | F-measure, Harmonic Mean | Hamming Distance, Subset Accuracy Loss |
| Terkait≠ | 5 | 1 |
| Ringkasan≠ | 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. | Hamming loss measures the fraction of labels that are incorrectly predicted in multi-label classification. It counts the number of label mistakes divided by the total number of labels, providing a simple metric for multi-label problems. |
| ScholarGateSet data ↗ |
|
|