Unverified50% confidenceOpinionExact time
1,900 categories may lead to a highly imbalanced category distribution, with some rare categories having very few samples that the model struggles to learn well.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
11/1/2026
First Seen
Valid until: 1/30/2027
Sources
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Related Claims
VerifiedIn scenarios with extreme class imbalance, even an accuracy as high as 95% may indicate that the model is completely unable to identify the minority class.72% similarUnverifiedIn models trained in practice, the weight distribution is often non-uniform, with a large number of weights tending toward zero, forming natural sparsity.67% similarUnverifiedIn actual surveys, cross-classification often creates a large number of sparse cells, with some cells containing only single-digit numbers of respondents or even zero, in which case the classical estimator based on the within-cell sample mean has extremely large variance.65% similarUnverified20 samples are insufficient to support a reliable multi-class model, and forcing subdivision will introduce noise and overfitting.65% 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.64% similar
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