Analyze customer data.
Choose a learning method based on labels and the objective.
Choose two appropriate mappings.
Use supervised learning for labeled prediction and consider clustering when finding groups in unlabeled data.
Detailed explanation
The model learns a relationship between inputs and known outcomes.
The model learns a relationship between inputs and known outcomes.
Clustering discovers similar groups without predefined labels.
Clustering discovers similar groups without predefined labels.
Without labels, ordinary accuracy cannot be calculated.
Without labels, ordinary accuracy cannot be calculated.
It needs inputs and their labels to learn a mapping.
It needs inputs and their labels to learn a mapping.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01試験ガイドDomain 1.1の教師あり・教師なし学習を確認する。Expected result
ラベルの有無と学習目的から方式を選択できる。Key points
- Labels
- Prediction
- 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.