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The video discusses the importance of finding and fixing labeling errors in training data to improve model performance. Labels often contain noise and errors due to human-generated inaccuracies and ambiguity. A systematic process is recommended to identify and correct these mistakes, using the trained model as a guide. The key question for ML and labeling teams is understanding how incorrect labels impact the model.
