SAS Exam A00-240 Topic 1 Question 84 Discussion

Actual exam question for SAS's A00-240 exam

Question #: 84
Topic #: 1

What is a benefit to performing data cleansing (imputation, transformations, etc.) on data after partitioning the data for honest assessment as opposed to performing the data cleansing prior to partitioning the data?

AIt makes inference on the model possible.

BIt is computationally easier and requires less time.

CIt omits the training (and test) data sets from the benefits of the cleansing methods.

DIt allows for the determination of the effectiveness of the cleansing method.

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Suggested Answer: D

by Novella at Apr 13, 2024, 04:13 AM

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Joesph

6 months ago

I think keeping the training and test data sets separate from the cleansing process is important for unbiased assessment.

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Theron

7 months ago

That's a good point, But doing it after allows us to focus on the effectiveness of the method itself.

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Leana

7 months ago

But wouldn't it be better to do the cleansing before partitioning to avoid bias in the data?

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Tom

7 months ago

I agree with It's important to know if the cleansing method is actually working.

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Theron

7 months ago

I think performing data cleansing after partitioning allows us to see how effective the cleansing method is.

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Valentin

8 months ago

Exactly! I mean, who cares if it takes a few extra minutes to run the analysis, as long as we're getting reliable results, right? I'd rather spend the time and do it right than rush through it and end up with a flawed model.

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Thaddeus

8 months ago

Haha, yeah, as long as you're not using a Commodore 64 to run your analysis, the computational time shouldn't be a problem. I'm with you, the honest assessment is way more important.

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Nell

8 months ago

That's true, but I think the benefits of getting a more accurate assessment outweigh the computational cost. Plus, modern computers are pretty powerful these days. I'm not too worried about the time it takes.

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Alyce

6 months ago

But option D) is also important. We need to know if the cleansing method is effective.

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Malinda

6 months ago

I agree with Trina. It's important to have clean data for accurate assessments.

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Trina

7 months ago

I think option A) makes sense. It allows us to make better inferences on the model.

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Jamie

8 months ago

I agree, but I'm also concerned about the computational expense. Wouldn't it be more efficient to do the cleansing before partitioning? That way, we don't have to do it multiple times for the training and test sets.

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Staci

8 months ago

Yeah, that's a good point. Doing it after partitioning also allows us to see how effective the cleansing methods are. That way, we can make a more informed decision on which techniques to use in the future.

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Wilda

8 months ago

Hmm, this is an interesting question. I think performing data cleansing after partitioning the data allows us to get a more honest assessment of the model's performance. If we do it beforehand, the cleansing methods might give the model an unfair advantage on the test set.

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