Known fraud and normal labels exist in one case; unknown behavior groups must be found in another.
Match method to purpose.
Choose two correct mappings.
Use supervised learning for known labels and unsupervised clustering to explore unlabeled structure, then validate cluster meaning.
Detailed explanation
The relationship between inputs and target labels is learned.
The relationship between inputs and target labels is learned.
Structure can be found without predefined classes.
Structure can be found without predefined classes.
The target and evaluation are missing.
The target and evaluation are missing.
Clusters still need interpretation and validation.
Clusters still need interpretation and validation.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.3の教師あり・教師なし学習とユースケースを確認する。Expected result
ラベルの有無と目的から学習方法を選び、結果の解釈を検証できる。Key points
- Supervised
- Unsupervised
- 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.