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This video explains how to use a model to find and correct labeling errors in training data. It emphasizes the importance of addressing labeling mistakes to improve model performance. Human-generated labels can contain noise and errors, even in well-known ML datasets. The text highlights the challenge of manually reviewing all labels and proposes using the trained model to identify labeling mistakes. The key question for ML teams is determining the impact of incorrect labels on the model. A systematic process is recommended for success, starting with looking for assets.
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