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DP-100 · Question #28

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might…

The correct answer is B. No. Although median imputation preserves dimensionality (no rows or columns are removed), it does not fully satisfy the goal of analyzing a 'full dataset to include all values.' Median imputation replaces every missing value with the same static value, which underestimates…

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Question

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are analyzing a numerical dataset which contains missing values in several columns. You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set. You need to analyze a full dataset to include all values. Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(37 responses)
  • A
    49% (18)
  • B
    51% (19)

Explanation

Although median imputation preserves dimensionality (no rows or columns are removed), it does not fully satisfy the goal of analyzing a 'full dataset to include all values.' Median imputation replaces every missing value with the same static value, which underestimates variance, distorts the distribution, and ignores relationships between features. For datasets with substantial missing data, this introduces significant bias. More sophisticated methods like MICE, which model missing values using the relationships among all features, are preferred in this scenario. The exam expects a statistically sound approach that truly represents data variability, not a simplistic single-value substitution.

Topics

#Missing values#Data imputation#Data cleaning#Feature preparation

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