Artificial intelligence · Grades 9–12
Training, validation and honest testing · revision notes
The model fits its parameters on training data. Success there does not alone demonstrate generalization to new cases.
Key ideas and conditions
Training
The model fits its parameters on training data. Success there does not alone demonstrate generalization to new cases.
Validation
Validation data supports choices such as model type, thresholds and hyperparameters. Repeated choices can overfit the validation process.
Testing
A held-out test set assesses the selected procedure. Do not repeatedly tune against it while continuing to call it untouched.
Metrics
Choose metrics that reflect important mistakes. Accuracy alone can hide failures on rare classes or specific groups.
Explain without looking
- Why does one good test result not guarantee performance in every future setting?
- Which is appropriate when reporting a model result?
Further reading and review references
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