Overfitting happens when a model learns the training data too well, including noise and irrelevant details, so it performs poorly on new data.
Why it matters:
- Produces AI that looks accurate in testing but fails in real-world use.
- A common mistake in machine learning.
Pro tip: Use techniques like cross-validation and larger, more diverse datasets to avoid overfitting.
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