Train an image classifier.
Choose models without using the same data for the final performance report.
Choose two correct statements.
Training data fits parameters, validation data supports model selection, and a held-out test set measures final generalization.
Detailed explanation
It is used for learning.
It is used for learning.
It allows development choices without consuming the final test set.
It allows development choices without consuming the final test set.
This adapts the model to the test set.
This adapts the model to the test set.
This does not measure performance on unseen data.
This does not measure performance on unseen data.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01試験ガイドDomain 1.3の訓練・検証・テスト分割を確認する。Expected result
各データ分割の目的とテスト汚染の問題を説明できる。Key points
- Training
- Validation
- Test
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.