overfitting
🤖 CT-AI
Official ISTQB Definition
A modeling error that occurs when a machine learning model learns the training data too well, including noise, resulting in poor performance on new data.
3 Ways to Think About It
The Quick Take
When an AI memorizes training data instead of learning general patterns - it passes training tests but fails in production.
Look Closer
The #1 enemy of ML models: performing perfectly on test data but badly on real-world data.
The Bottom Line
An AI that's too specialized to its training examples, caught by testing on held-out data it's never seen.
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