Validation Set
Definition
A validation dataset helps in selecting and tuning a model during its development.
It is separated from the actual training data and used for decisions about models or settings. An independent test dataset remains reserved for the final evaluation.
Simply explained
Like practice exercises for choosing a learning method, while the final exam is still withheld.
Why it matters in practice
Models can be compared without measuring their performance only on known training examples.
Example from business
Hypothetical example: A team selects the settings of a ticket classifier based on withheld examples.
Limits and pitfalls
- Frequent adjustment to the same validation data can reduce its informativeness; data leaks distort the result.
Origin & history
The separation of training, validation, and test data is part of statistical model evaluation.
Use in mid-sized companies
- Systematically select models and hyperparameters.
Frequently asked questions
Are validation and test data interchangeable?
No, their roles should remain separate in the development workflow.
Sources
- Cross-validation · scikit-learn: Cross-validation –
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