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Overfitting
過学習(かがくしゅう)
IntermediateCore Concepts
When a model learns the training data too well, including its noise and quirks, and performs poorly on new, unseen data.
Why It Matters
Overfitting means your model memorized answers instead of learning patterns — it won't generalize to real-world use.
Example in Practice
A model that scores 99% on training data but only 60% on new test data.