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in this video well see how you can use model to find and fix labeling errors first off your model is only as good as your training data and a great way to boost model performance is to find and fix the labeling mistakes in your training data its more common than you think for labels to contain noise and errors even the most canonical ml data sets out there have quite a lot of labeling mistakes why is that because labels are generated by humans and humans can sometimes make mistakes some labels are also inherently ambiguous and lastly its impossible to review all of your labels manually or to have them checked by a panel of labelers so the bottom line is that this labeling noise can undermine your model a key question for ML and labeling teams is where are my labels wrong and where is it impacting my model you can use your trained model as a guide to find labeling mistakes so heres a systematic process that weve seen success with over and over again Step One is looking for assets
