overfitting
What It Means
Overfitting occurs when an AI model becomes too specialized to its training data, memorizing specific patterns rather than learning generalizable rules. The model performs exceptionally well on the data it was trained on but fails to perform accurately when encountering new, real-world data it hasn't seen before.
Why Chief AI Officers Care
Overfitted models create significant business risk because they appear to work perfectly during development and testing but fail catastrophically when deployed to real customers or operations. This leads to poor customer experiences, failed AI initiatives, wasted development resources, and potential regulatory compliance issues if the model's poor real-world performance affects critical business decisions.
Real-World Example
A fraud detection system trained on historical transaction data achieves 99% accuracy in testing but only 60% accuracy after deployment because it memorized specific account numbers and merchant names from the training data rather than learning actual fraud patterns, causing it to miss new types of fraudulent transactions while flagging legitimate customers.
Common Confusion
People often confuse overfitting with simply having a complex model, but complexity alone doesn't cause overfitting. A model can be highly complex yet still generalize well to new data, while a simple model can still overfit if it memorizes training examples instead of learning underlying patterns.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, overfitting poses critical safety risks when diagnostic models memorize training datasets rather than ...
Finance: In finance, overfitting commonly occurs when algorithmic trading models or credit risk models are trained on historical ...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Given a hypothesis space H, a hypothesis h element of H is said to overfit the training data if there exists some alternative hypothesis h' element of H, such that h has smaller error than h' over the training examples, but h' has a smaller error than h over the entire distribution of instance."Source: Mitchell,_Tom
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