Before deploying a model into production in the Data Science Life Cycle, it's essential to have a _______ phase to test the model's real-world performance.
- Training phase
- Deployment phase
- Testing phase
- Validation phase
Before deploying a model into production, it's crucial to have a testing phase to evaluate the model's real-world performance. This phase assesses how the model performs on unseen data to ensure its reliability and effectiveness.
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