MCDMClassification Metric
Akurasi Seimbang
Akurasi seimbang ialah purata nilai perolehan (recall) yang dikira bagi setiap kelas secara berasingan. Ia membetulkan ketidakseimbangan kelas dengan memberi pemberat yang sama kepada prestasi pada setiap kelas, tanpa mengira kekerapan kelas dalam set data.
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Ahli sahaja
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Method map
The neighbourhood of related methods — select a node to explore.
Sumber
- 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: 10.1109/ICPR.2010.764 ↗
- 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 ↗
Cara memetik halaman ini
ScholarGate. (2026, June 3). Balanced Classification Accuracy. ScholarGate. https://scholargate.app/ms/model-evaluation/balanced-accuracy
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- KetepatanPenilaian Model↔ compare
- Skor F1Penilaian Model↔ compare
- Koefisien Korelasi MatthewsPenilaian Model↔ compare
- Deria (Sensitiviti)Penilaian Model↔ compare
- Ketepatan (Specificity)Penilaian Model↔ compare
Dirujuk oleh
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