SAS Exam A00-240 Topic 7 Question 87 Discussion

Actual exam question for SAS's A00-240 exam

Question #: 87
Topic #: 7

When mean imputation is performed on data after the data is partitioned for honest assessment, what is the most appropriate method for handling the mean imputation?

AThe sample means from the validation data set are applied to the training and test data sets.

BThe sample means from the training data set are applied to the validation and test data sets.

CThe sample means from the test data set are applied to the training and validation data sets.

DThe sample means from each partition of the data are applied to their own partition.

Show Suggested Answer

Suggested Answer: C

by Lorita at May 11, 2024, 01:12 AM

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Kindra

1 months ago

Mean imputation, the data scientist's version of 'fake it till you make it'!

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Gracia

11 days ago

A) The sample means from the validation data set are applied to the training and test data sets.

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Kizzy

1 months ago

Option D all the way! Anything else would be like trying to fit a square peg in a round hole.

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Latrice

23 days ago

I agree, using the sample means from each partition of the data for mean imputation makes the most sense.

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Arthur

29 days ago

Option D all the way! Anything else would be like trying to fit a square peg in a round hole.

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Deeanna

2 months ago

As a data scientist, I'd choose Option D. Maintaining the integrity of the partitions is crucial for an honest model assessment.

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Portia

5 days ago

I see your point, but I still believe Option D is the best choice for maintaining the honesty of the assessment.

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Hildred

18 days ago

I think Option B would be more appropriate, as it applies the sample means from the training data set to the validation and test data sets.

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Marguerita

1 months ago

I agree, Option D ensures that each partition maintains its own integrity.

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Alyce

2 months ago

I'm not sure, but I think D) The sample means from each partition of the data are applied to their own partition could also be a valid approach to maintain the integrity of the data.

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Ressie

2 months ago

I agree with Maile. It makes sense to use the sample means from the training data set for consistency across the different partitions.

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Maile

2 months ago

I think the most appropriate method is B) The sample means from the training data set are applied to the validation and test data sets.

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Sylvie

2 months ago

I see both points, but I think D) The sample means from each partition of the data should be applied to their own partition makes the most sense for unbiased results.

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Nicholle

2 months ago

I disagree, I believe B) The sample means from the training data set should be applied to the validation and test data sets.

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Ciara

2 months ago

I was initially leaning towards Option B, but Option D makes more sense. Applying the training set means to the validation and test sets could introduce bias.

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Kerry

1 months ago

Yeah, it's important to avoid introducing bias by using means from the training set for the validation and test sets.

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Glory

1 months ago

It's important to avoid introducing bias when handling mean imputation after partitioning the data.

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Karon

1 months ago

Option D seems like the most appropriate method for mean imputation.

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Maryrose

1 months ago

I agree, using the means from each partition keeps the data unbiased.

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Lizette

1 months ago

I agree, using the means from each partition seems like the most unbiased approach.

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Adela

2 months ago

I think Option D is the best choice.

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Kimberely

2 months ago

I think the most appropriate method is A) The sample means from the validation data set are applied to the training and test data sets.

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Helga

2 months ago

Option D seems like the correct choice here. Applying the sample means from each partition to their own partition is the most appropriate way to handle mean imputation after partitioning the data.

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