You have an Azure Machine Learning model that generates a large quantity of false negatives. You need to reduce the number of false negatives without re-training the model. What should you do?

The correct answer is C. Adjust the threshold value.. What are different methods to minimize False Negatives and False Positives in Binary One of the easiest methods to minimize the outcomes of a certain case is simply changing the decision boundary line from the basic 0.5 to above (when reducing False Positives) or below (when redu

Submitted by rachelw· Mar 30, 2026Describe fundamental principles of machine learning on Azure

Question

You have an Azure Machine Learning model that generates a large quantity of false negatives. You need to reduce the number of false negatives without re-training the model. What should you do?

Options

AUse a different Machine Learning model.
BIncrease the amount of training data.
CAdjust the threshold value.
DIncrease the number of training iterations.

How the community answered

(27 responses)

A
4% (1)
B
11% (3)
C
81% (22)
D
4% (1)

Explanation

What are different methods to minimize False Negatives and False Positives in Binary One of the easiest methods to minimize the outcomes of a certain case is simply changing the decision boundary line from the basic 0.5 to above (when reducing False Positives) or below (when reducing False Negatives). It should be noted that by doing this, the possibility of False Positives increases. In other words, by decreasing the False Negatives we are increasing the False Positives.

Topics

#false negatives#threshold adjustment#classification model#model tuning

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