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
Clusters still need interpretation and validation.
The relationship between inputs and target labels is learned.
The target and evaluation are missing.
Structure can be found without predefined classes.
Check official documentation
Compare the following points with the official documentation.
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。
Official references
What to confirm
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
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Supervised learning
Learning from examples that pair inputs with known target labels or values.
Unsupervised learning
Finding structure or groups in data without target labels.