Unverified50% confidenceFactExact time
Theoretically reliable clustered standard error estimation typically requires at least 30–50 clusters, whereas this study has only 19 independent time units, making the sample size relatively small.
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50%
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Long-term
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9/22/2026
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Unverified20 samples are insufficient to support a reliable multi-class model, and forcing subdivision will introduce noise and overfitting.74% similarUnverifiedIn few-shot scenarios (e.g., approximately 20 samples per class), multi-class subdivision should be abandoned, all defects should be collapsed into a binary classification problem (defective/non-defective), and specific classification should be left to manual post-processing.71% similarUnverifiedThe accuracy data in the test has an error margin of approximately ±8–10 percentage points at n=100 sample size (based on a 95% confidence-level binomial confidence interval).70% similar
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