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The video tutorial discusses the importance of fixing labeling errors in training data to improve model performance. Labels often contain noise and errors due to human mistakes and ambiguity. It is impossible to manually review all labels, which can impact the model's accuracy. The key question is identifying where labeling mistakes occur and how they affect the model. One method is to use the trained model to detect errors systematically. Step One is looking for assets.
