New Year Sale ! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

iSQI Exam CT-AI Topic 4 Question 14 Discussion

Actual exam question for iSQI's CT-AI exam
Question #: 14
Topic #: 4
[All CT-AI Questions]

Data used for an object detection ML system was found to have been labelled incorrectly in many cases.

Which ONE of the following options is most likely the reason for this problem?

SELECT ONE OPTION

Show Suggested Answer Hide Answer
Suggested Answer: B

The question refers to a problem where data used for an object detection ML system was labelled incorrectly. This issue is most closely related to 'accuracy issues.' Here's a detailed explanation:

Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.

Why Not Other Options:

Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.

Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.

Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.


Contribute your Thoughts:

Kirk
2 months ago
Bias issues, definitely. Unless the data was purposefully mislabeled to, I don't know, mess with the system or something. In that case, maybe security issues? Nah, bias all the way.
upvoted 0 times
Jamey
29 days ago
It's important to address bias issues to ensure the accuracy of the object detection system.
upvoted 0 times
...
Lorenza
1 months ago
Maybe the data collectors had certain biases that affected the labeling.
upvoted 0 times
...
Rodney
1 months ago
Yeah, bias can really skew the results of the ML system.
upvoted 0 times
...
Johnna
1 months ago
I agree, bias could definitely be a major factor in the mislabeling.
upvoted 0 times
...
...
Lilli
2 months ago
Hmm, security issues? I guess if the data was tampered with, but that's a bit of a stretch. I'll go with the good old D) Bias issues.
upvoted 0 times
...
Aliza
2 months ago
Privacy issues? Really? I don't see how that's related to mislabeled data. I'm going with D) Bias issues.
upvoted 0 times
Lilli
1 months ago
User 3: I think we need to address bias issues to improve the accuracy of the ML system.
upvoted 0 times
...
Oretha
1 months ago
User 2: Yeah, bias can definitely lead to incorrect labeling in object detection systems.
upvoted 0 times
...
Paola
1 months ago
I agree, I think bias issues are more likely the reason for mislabeled data.
upvoted 0 times
...
...
Bernardo
3 months ago
B) Accuracy issues, of course! Incorrect labeling is going to tank the performance of the object detection model. Duh!
upvoted 0 times
...
Meghan
3 months ago
I think the answer is D) Bias issues. Incorrect labeling can introduce biases into the ML system, leading to inaccurate object detection.
upvoted 0 times
Frankie
2 months ago
Correct labeling is crucial for the performance of the ML system, so bias issues must be carefully considered.
upvoted 0 times
...
Val
2 months ago
It's important to address bias issues in the data to ensure accurate object detection.
upvoted 0 times
...
Tori
2 months ago
I agree, D) Bias issues can definitely lead to incorrect labeling in the data.
upvoted 0 times
...
...
Gerald
3 months ago
But what about accuracy issues? Could that also be a reason for the problem?
upvoted 0 times
...
Caitlin
3 months ago
I agree with Dorethea, bias issues can lead to incorrect labelling in the data.
upvoted 0 times
...
Dorethea
3 months ago
I think the reason for incorrect labelling could be bias issues.
upvoted 0 times
...

Save Cancel