Microsoft
AI-900 · Question #279
What should you do to reduce the number of false positives produced by a machine learning classification model?
The correct answer is D. Modify the threshold value in favor of false negatives.. If you have a classifier which calculates a real values score and then a threshold is applied to define what is classified as positive or negative. By changing this threshold you can decrease the number of false positives at the expense of increasing the number of false negatives
Submitted by dimitri_ru· Mar 30, 2026Describe fundamental principles of machine learning on Azure
Question
What should you do to reduce the number of false positives produced by a machine learning classification model?
Options
- AInclude test data in the training data.
- BIncrease the number of training iterations.
- CModify the threshold value in favor of false positives.
- DModify the threshold value in favor of false negatives.
How the community answered
(34 responses)- A24% (8)
- B6% (2)
- C9% (3)
- D62% (21)
Explanation
If you have a classifier which calculates a real values score and then a threshold is applied to define what is classified as positive or negative. By changing this threshold you can decrease the number of false positives at the expense of increasing the number of false negatives.
Topics
#classification model#false positives#threshold tuning#model evaluation
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