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SAS A00-240 Exam - Topic 7 Question 87 Discussion

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?
C) The sample means from the test data set are applied to the training and validation data sets.
A) The sample means from the validation data set are applied to the training and test data sets.
B) The sample means from the training data set are applied to the validation and test data sets.
D) The sample means from each partition of the data are applied to their own partition.

SAS A00-240 Exam - Topic 7 Question 87 Discussion

Actual exam question for SAS's A00-240 exam
Question #: 87
Topic #: 7
[All A00-240 Questions]

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?

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Suggested Answer: C

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

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Floyd
6 months ago
Not sure about D, feels like it could lead to bias.
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Renea
7 months ago
I agree with B, it's the standard approach for mean imputation.
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Remona
7 months ago
Wait, can we really use test data means? That seems off.
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Patti
7 months ago
I think D makes more sense, each partition should handle its own data.
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Frankie
7 months ago
Definitely B, training means should be used for validation and test.
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Annice
8 months ago
I’m leaning towards option B, but I’m a bit confused about why we wouldn’t use the test set means instead.
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Gail
8 months ago
Isn't it risky to apply means from the validation set to the training data? I feel like that could lead to data leakage.
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Cherri
8 months ago
I remember practicing a similar question, and I believe it's important to avoid using validation data for imputation.
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Thea
8 months ago
I think we should use the training data means for the validation and test sets, but I'm not completely sure.
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Lashon
8 months ago
This seems straightforward to me. Since the data is partitioned, the sample means from each partition should be used to impute the missing values in that same partition. That's the only way to maintain the integrity of the assessment process. I'm confident option D is the correct answer.
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Destiny
8 months ago
Okay, I've got this. The key is that the mean imputation should be done separately for each partition of the data. That way, the training, validation, and test sets all have their own unique mean imputations applied. Option D is the way to go.
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Kimbery
8 months ago
Hmm, I'm a little confused on this one. I know mean imputation is a common way to handle missing data, but I'm not sure how that interacts with the data partitioning. I'll have to think this through carefully.
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Ashton
8 months ago
This question is a bit tricky, but I think I have a good strategy. Since the data is partitioned for honest assessment, the mean imputation should be done within each partition to avoid data leakage.
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Dean
8 months ago
Okay, I think I have a good handle on this. The key is to identify how the other acts relate to or build upon the GDPR framework. Analyzing the specific language used in the question will be crucial.
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Carey
8 months ago
I remember discussing service statelessness in class, and it seems like that might be the principle we need here to reduce memory load.
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Tamra
9 months ago
This looks like a tricky one! I'll need to carefully read through the steps and make sure I understand what's going on with the unexpected error.
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Kindra
1 year ago
Mean imputation, the data scientist's version of 'fake it till you make it'!
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Karl
12 months ago
D) The sample means from each partition of the data are applied to their own partition.
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Alishia
12 months ago
B) The sample means from the training data set are applied to the validation and test data sets.
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Gracia
1 year 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 year 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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Sunshine
12 months ago
It's important to maintain the integrity of the partitions when performing mean imputation, so Option D is definitely the way to go.
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Latrice
1 year 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
1 year 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
1 year 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
1 year 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
1 year 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 year ago
I agree, Option D ensures that each partition maintains its own integrity.
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Alyce
1 year 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
1 year 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
1 year 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
1 year 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
1 year 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
1 year 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 year 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 year ago
It's important to avoid introducing bias when handling mean imputation after partitioning the data.
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Karon
1 year ago
Option D seems like the most appropriate method for mean imputation.
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Maryrose
1 year ago
I agree, using the means from each partition keeps the data unbiased.
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Lizette
1 year ago
I agree, using the means from each partition seems like the most unbiased approach.
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Adela
1 year ago
I think Option D is the best choice.
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Kimberely
1 year 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
1 year 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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