Problem
Scenario
You've trained a machine learning model and offline results look promising. Before putting it into production, your team wants a clear validation process that checks whether the model is reliable and fit for real use.
Question
How do you validate a machine learning model before deployment?
What This Tests
- Cross-validation for generalization checks
- Precision and recall tradeoffs at the operating threshold
- Confusion matrix interpretation for business impact
- Calibration of predicted probabilities before deployment
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