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The video tutorial discusses how to use a model to identify and correct labeling errors in training data. It highlights the importance of data quality in enhancing model performance, as labeling mistakes are common due to human error and ambiguity. The text emphasizes the need to address labeling noise that can undermine model accuracy. A systematic process is recommended to detect and rectify labeling errors using the trained model as a guide. This approach is proven successful in identifying incorrect labels and their impact on the model.
