Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| F1 Makro× | Skor-F1× | |
|---|---|---|
| Bidang | Evaluasi Model | Evaluasi Model |
| Keluarga | MCDM | MCDM |
| Tahun asal≠ | 2000s | 1979 |
| Pencetus≠ | Multi-class evaluation community | C. J. van Rijsbergen |
| Tipe | Evaluation metric | Evaluation metric |
| Sumber perintis≠ | Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63. link ↗ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ |
| Alias | Macro F1, Unweighted average F1 | F-measure, Harmonic Mean |
| Terkait≠ | 3 | 5 |
| Ringkasan≠ | Macro-averaged F1 computes the F1-score independently for each class and then takes the unweighted arithmetic mean. It treats all classes equally, regardless of their frequency in the dataset, making it useful for imbalanced multi-class problems. | 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 ↗ |
|
|