Labeled data is curated training data where each item includes both the input and the correct output. In return classification, this means customer text paired with the human-assigned return reason. Creating labeled data requires human effort and expertise, making it expensive to produce at scale. Most AI classifiers need thousands to hundreds of thousands of labeled examples to perform well.
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Labeled Data
Training examples where humans have already assigned the correct category or answer.
Related terms
- Training Data — The labeled examples used to teach an AI model how to categorize returns correctly.
- AI Classifier — A machine learning model that automatically categorizes inputs—like return reasons or customer feedback—into predefined groups.
- Return Reason Code — A standardized label assigned to each return describing why the customer sent the item back.
- Model Accuracy — The percentage of classifications the AI model gets correct across all categories.