Training, Validation, and Testing in Machine Learning

Why data splits exist

Machine learning models can look good during development and still fail in the real world. Data splits help reduce that risk.

The three roles

Training data is what the model learns from. Validation data helps you compare model choices. Test data is held back until the end so you can estimate how the model performs on unseen data.

• Do not tune your model directly on the test set.
• Use validation results to make development decisions.
• Treat the test result as a final check, not a playground.

The builder mindset

Good evaluation is a product habit, not only a research habit. If an AI feature affects users, your evaluation setup should match the real use case as closely as possible.