An organization is using machine learning to make predictions. One of their datasets mistakenly includes mislabeled data. How will the prediction be impacted?
Mislabeled data can introduce errors and inconsistencies into the training process, which can lead to inaccurate predictions. When the model learns from mislabeled examples, it may develop incorrect patterns or associations, compromising its ability to make accurate predictions on new, unseen data.
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