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CompTIA DY0-001 Exam - Topic 1 Question 19 Discussion

A data scientist is using the following confusion matrix to assess model performance:The model is predicting whether a delivery truck will be able to make 200 scheduled delivery stops. Every time the model is correct, the company saves an hour in planning and scheduling of maintenance work. Every time the model is wrong, the company loses four hours of delivery time for the truck. Which of the following is the net model impact for the company?
A) 25 hours lost
B) 25 hours saved
C) 165 hours lost
D) 165 hours saved

CompTIA DY0-001 Exam - Topic 1 Question 19 Discussion

Actual exam question for CompTIA's DY0-001 exam
Question #: 19
Topic #: 1
[All DY0-001 Questions]

A data scientist is using the following confusion matrix to assess model performance:

The model is predicting whether a delivery truck will be able to make 200 scheduled delivery stops. Every time the model is correct, the company saves an hour in planning and scheduling of maintenance work. Every time the model is wrong, the company loses four hours of delivery time for the truck. Which of the following is the net model impact for the company?

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Suggested Answer: A

Treat each ''predicted-to-fail'' and ''predicted-to-succeed'' row as coming from 100 cases apiece (200 total).


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Truman
3 hours ago
The model saves 1 hour for each correct prediction.
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Luisa
5 days ago
I vaguely recall that we should multiply the true positives by the hours saved and the false negatives by the hours lost. I hope I remember the right numbers!
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Jonell
10 days ago
I think if we calculate the total hours saved and lost, we can find the net impact. But I'm a bit confused about how to weigh the hours differently for correct and incorrect predictions.
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Erinn
16 days ago
This question feels similar to one we practiced about cost-benefit analysis in model evaluation. I think I need to focus on the hours saved versus lost.
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Nicholle
2 months ago
I remember we discussed how to calculate the net impact based on true positives and false negatives, but I'm not sure how to apply it here.
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