Multi-class classification means the model chooses from more than two possible categories. Instead of just 'defective' or 'not defective,' multi-class models assign reasons like 'damaged product,' 'wrong item,' 'size issue,' 'not as described,' or 'changed mind.' Most return reason classification tasks are multi-class problems requiring models that can distinguish between dozens of nuanced categories.
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Multi-class Classification
A classification task where each input can be assigned one of three or more possible categories.
Related terms
- AI Classifier — A machine learning model that automatically categorizes inputs—like return reasons or customer feedback—into predefined groups.
- Return Reason Classification — The process of categorizing why customers return products using AI to analyze descriptions, images, or structured data.
- Training Data — The labeled examples used to teach an AI model how to categorize returns correctly.
- Model Accuracy — The percentage of classifications the AI model gets correct across all categories.