You've built a classification model, but it's highly sensitive to changes in the test data. What could be the issue and how would you fix it?
- Overfitting; Cross-validation
- Overfitting; Increase regularization
- Underfitting; Add more features
- Underfitting; Use different model
The issue could be overfitting, where the model performs well on training data but poorly on unseen data. Fixing this might involve using cross-validation to ensure the model generalizes well to new data.
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