Standard 2 stops to get here
Out-of-Distribution
Data that differs significantly from the training distribution, where models often perform poorly or unreliably.
Your route here
2 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training Data ✓ understood
The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.
- Out-of-Distribution · you are here ✓ understood
Where it sits
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Evaluation Robustness A model's ability to maintain performance under distribution shifts, adversarial attacks, or noisy inputs. Shipping AI Data Drift Changes in input data distribution over time that can degrade model performance in production. Shipping AI Model Drift Degradation of model performance over time due to changes in the relationship between features and target. Evaluation Calibration Ensuring predicted probabilities accurately reflect true likelihood of outcomes. Shipping AI Prediction Confidence A measure of model certainty in its predictions, important for reliability and user trust.