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How to Measure Real Model Accuracy When Labels Are Noisy
- Ground truth is never perfect, with errors in measurements and human annotations, raising concerns on evaluating models using imperfect labels.
- Exploring methods to estimate a model's 'true' accuracy when labels are noisy is essential.
- Errors in both model predictions and ground truth labels can mislead accuracy measurements.
- The true accuracy of a model can vary based on error correlations between the model and ground truth labels.
- Indications show that the model's true accuracy depends on the overlap of errors with ground truth errors.
- If errors are uncorrelated, a probabilistic estimate formula can help derive a more precise true accuracy.
- In practices where errors may be correlated, the true accuracy tends to lean towards the lower bound.
- Understanding the difference between measured and true accuracy is crucial for accurate model evaluation.
- Targeted error analysis and multiple independent annotations are recommended for handling noisy labels in model evaluation.
- In summary, the range of true accuracy depends on ground truth error rates, with considerations for error correlations in real-world scenarios.
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