Evaluate a fraud classifier.
Reduce missed fraud while also tracking false alarms.
Choose two correct statements.
Precision is the fraction of positive predictions that are truly positive; recall is the fraction of actual positives that were detected.
Detailed explanation
Precision measures the correctness of positive predictions.
Precision measures the correctness of positive predictions.
Recall measures how much of the actual positive class was found.
Recall measures how much of the actual positive class was found.
Recall and false positives are different aspects and can involve a trade-off.
Recall and false positives are different aspects and can involve a trade-off.
Precision and recall are classification metrics based on a confusion matrix.
Precision and recall are classification metrics based on a confusion matrix.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドのDomain 3.3で分類評価指標の意味を確認する。Expected result
適合率と再現率を分母の違いで説明し、見逃し・誤検知との関係を整理できる。Key points
- Precision
- Recall
- Confusion matrix
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.