Some transaction records have fraud labels.
Another dataset has no labels and should be grouped by purchasing behavior.
Choose two statements that correctly describe supervised or unsupervised learning.
Supervised learning uses labels for prediction or classification, while unsupervised learning discovers structure without labels.
Detailed explanation
Known labels provide the target for supervised learning.
Known labels provide the target for supervised learning.
Groups are discovered from similarity without target labels.
Groups are discovered from similarity without target labels.
Hand-written rules are not a model learned from data.
Hand-written rules are not a model learned from data.
Supervised learning uses labels, so this reverses the definition.
Supervised learning uses labels, so this reverses the definition.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドのDomain 1.1で、教師あり学習と教師なし学習のユースケースを確認する。Expected result
ラベルの有無と、予測・分類または構造発見の目的を対応付けられる。Key points
- Labeled data
- Unlabeled data
- Classification and clustering
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.