Build a classifier from labeled data.
Separate model selection from final performance evaluation.
Choose two correct statements.
Training data learns parameters, validation data supports development choices, and a held-out test set is reserved for final evaluation without overlap.
Detailed explanation
The model learns patterns from these examples.
The model learns patterns from these examples.
Development and final evaluation remain separate.
Development and final evaluation remain separate.
The test set becomes part of development.
The test set becomes part of development.
The test is no longer unknown.
The test is no longer unknown.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.2のデータ分割、モデル評価、過学習を確認する。Expected result
訓練・検証・テストの役割とデータ漏えいの危険を説明できる。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.