Why are labels important in supervised learning?

How It Works:

Labels assign ground-truth values to data samples-e.g., ?cat? vs. ?dog?-enabling models to learn input-to-output mappings during training.

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Key Benefits:

• Supervised signal: Guides model learning.
• Evaluation benchmark: Measures accuracy against known truths.
• Data quality: Good labels make strong models.

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Real-World Use Cases:

• Image datasets: Annotators mark bounding boxes.
• Text classification: Tagging sentiment in reviews.