distribution shift

**Distribution Shift** is the **discrepancy between the data distribution during training and the distribution encountered during deployment** — when $P_{test}(X, Y) eq P_{train}(X, Y)$, model performance degrades, sometimes catastrophically, because the model has not learned to handle the new data characteristics. **Types of Distribution Shift** - **Covariate Shift**: $P(X)$ changes but $P(Y|X)$ stays the same — input distribution changes. - **Label Shift**: $P(Y)$ changes but $P(X|Y)$ stays the same — class proportions change. - **Concept Drift**: $P(Y|X)$ changes — the relationship between inputs and outputs changes over time. - **Domain Shift**: The data comes from a different domain (different fab, sensor, process recipe). **Why It Matters** - **Silent Degradation**: Models fail silently under distribution shift — accuracy drops without obvious errors. - **Semiconductor**: Process drift, tool degradation, new products all cause distribution shift — models must handle it. - **Monitoring**: Continuous monitoring for distribution shift is essential in production ML systems. **Distribution Shift** is **the world changed but the model didn't** — performance degradation when deployment data differs from training data.