← Library · Core concept
Overfitting
Overfitting occurs when an AI model learns the training data too well, memorizing noise and specific examples rather than general patterns. This leads to excellent performance on the training set but poor generalization to new, unseen data. The model becomes too specialized to its training examples.
In plain terms
Overfitting is like studying for a test by memorizing every example problem in the textbook without understanding the underlying concepts.
Why it matters
It prevents AI models from being reliably useful in real-world applications where they encounter novel information.
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