Out-of-Distribution
Out-of-Distribution (OOD) data refers to inputs that originate from a different probability distribution than the dataset used to train the machine learning model, often causing models to make confident mistakes.
Frequently Asked Questions
Why do models fail on out-of-distribution inputs?▼
Because models make predictions based on statistical correlation patterns learned during training, which do not hold true for fundamentally different data.
How do you detect out-of-distribution inputs?▼
By monitoring embedding distances or using classification confidence thresholds to route anomalous inputs to safety loops.
Quick Facts
- CategoryModel Limitations
- Key ApplicationRobustness validation runs, anomaly detection systems, and model safety checks.
Coverage Trend12 Weeks
Related AI Terms
Out-of-Distribution Media Coverage & Intelligence
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