What is the difference between training data, validation data, and test data?
Fundamental concept question about data splitting and the purpose of each subset in the ML workflow.
Why Interviewers Ask This
Proper data splitting is critical for unbiased model evaluation. Interviewers check if you understand the distinct roles of each set to prevent data leakage and overfitting.
How to Answer This Question
Define Training set (model fitting), Validation set (hyperparameter tuning), and Test set (final evaluation). Emphasize that the test set should never influence training or tuning decisions.
Key Points to Cover
- Define purpose of each set
- Prevent data leakage
- Ensure unbiased evaluation
Sample Answer
Training data is used to fit the model parameters. Validation data is used to tune hyperparameters and select the best model architecture during development. The test data is held out completely until the end to provide an unbiased estimate of the model's performance on real-world data. Mixing these up leads to optimistic bias.
Common Mistakes to Avoid
- Using test data for tuning
- Confusing validation and test sets
- Not splitting data at all
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