A fraud detector should reduce missed fraud.
A home-price model should reduce the difference between predictions and actual values.
Choose two correct task-metric pairs.
Use recall to assess missed fraud and a regression error such as mean absolute error for price predictions.
Detailed explanation
Recall measures how much actual fraud is detected.
Recall measures how much actual fraud is detected.
Mean absolute error measures the average absolute difference from the actual value.
Mean absolute error measures the average absolute difference from the actual value.
A confusion matrix is a classification tool, not a direct continuous-value metric.
A confusion matrix is a classification tool, not a direct continuous-value metric.
Class imbalance can hide missed fraud in overall accuracy.
Class imbalance can hide missed fraud in overall accuracy.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドDomain 1.3のモデル評価指標を確認する。Expected result
分類と回帰で指標を分け、業務上重要な誤りを指標へ反映できる。Key points
- Metric selection
- Missed positives
- Regression error
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.