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An IS auditor notes that an AI model achieved significantly better results on training data than on test dat
a. Which of the following problems with the model has the IS auditor identified?
Suggested Answer: B
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18 comments · newest first
DO
Domonique
10 months ago
Not sure, but bias could play a role too, right?
LA
Laquita
10 months ago
Generalization problems can also cause this, but overfitting fits better.
CA
Catarina
10 months ago
I agree, overfitting is the issue here.
FR
Frederic
10 months ago
Wait, could it be underfitting instead?
CA
Casie
11 months ago
Definitely overfitting! The model's memorizing the training data.
BE
Beatriz
11 months ago
I feel like bias could be involved too, but I don't recall it being specifically about performance differences between training and test data.
BR
Brock
11 months ago
I practiced a question similar to this, and it was definitely about overfitting. The model memorizes the training data instead of learning patterns.
OM
Omer
11 months ago
I'm not entirely sure, but I think generalization might be related to how well the model applies what it learned. This could be a case of that, right?
CR
Craig
12 months ago
I remember discussing overfitting in class, where the model performs well on training data but poorly on new data. I think that's the issue here.
GE
Germaine
12 months ago
This is a tricky one. Overfitting and underfitting can be easy to mix up. I'll need to carefully think through the implications of the model's performance on the training versus test data to determine the right answer.
CH
Cherry
12 months ago
Okay, I think I've got this. The key is to identify whether the model is overfitting or underfitting based on the performance gap between training and test data. Overfitting is the more likely culprit here.
JU
Julie
1 year ago
Hmm, I'm a bit confused here. Is it possible the model is underfitting instead? I'll need to review the concepts of overfitting and underfitting to make sure I understand the distinction.
LO
Louvenia
1 year ago
This seems like a classic case of overfitting. The model has performed too well on the training data, but failed to generalize to the test data. I'll need to carefully consider how to avoid this pitfall.
VA
Vanda
1 year ago
I agree with Basilia, the model is probably overfitting the training data.
BA
Basilia
1 year ago
I think the IS auditor identified overfitting.
NI
Nickolas
1 year ago
Hmm, I'd say it's a case of overfitting. The model is too heavily influenced by the training data and can't handle the test set properly.
AR
Arlen
1 year ago
Overfitting, for sure! The model is doing too well on the training data and not generalizing to the test set. Gotta watch out for that!
WH
Whitley
1 year ago
I agree, overfitting can be a big issue with AI models.