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Balanced accuracy is calculated as the arithmetic mean of per-class recall rates and is used to address class imbalance problems.
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10/9/2026
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UnverifiedBenchmark scores measure the average of a model's success consistency and failure frequency, not its peak capability.72% similarUnverifiedIn class-imbalance scenarios, a model can achieve seemingly impressive accuracy by always predicting the majority class, a phenomenon known as the Accuracy Paradox.70% similarUnverifiedReflected symmetrization is a variance reduction technique in statistical estimation that eliminates one-sided bias by averaging an estimator with its symmetrically flipped version69% similarUnverifiedIn imbalanced classification problems, a model can achieve very high accuracy even by predicting all samples as the majority class, so it is necessary to shift to more discriminative metrics such as precision-recall curves, F1 score, Matthews correlation coefficient, or AUC-ROC69% similarUnverifiedReducing the LoRA weight (typically from the default 1.0 to 0.6–0.8) combined with 6–8 inference steps is a compromise that finds a better balance between efficiency and quality.69% similar
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