Explanation:
The correct answer is A because it aligns with the performance curve analysis and the given scenario parameters. The current model uses a cut threshold of 0.45, and retraining occurs if weighted Kappa deviates from 0.1 ± 5%. Option A sets the threshold to 0.5 (a reasonable adjustment from 0.45) and maintains the retraining condition based on weighted Kappa deviation from 0.45 ± 5%, which is consistent with optimizing model performance while managing costs. The community discussion shows 100% consensus on A, with upvoted comments supporting this choice. Other options are less suitable: B (threshold 0.05) is too low and may increase false positives, C (threshold 0.2) shifts the threshold too far from the current 0.45, and D (threshold 0.75) is too high and may miss true positives, both deviating from the scenario's implied balance between precision and recall.
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You need to implement a new cost factor scenario for the advertisement response models as shown in the performance curve exhibit.
Which technique should you use?
A
Set the threshold to 0.5 and retrain if weighted Kappa deviates +/- 5% from 0.45.
B
Set the threshold to 0.05 and retrain if weighted Kappa deviates +/- 5% from 0.5.
C
Set the threshold to 0.2 and retrain if weighted Kappa deviates +/- 5% from 0.6.
D
Set the threshold to 0.75 and retrain if weighted Kappa deviates +/- 5% from 0.15.