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DP-100 · Question #26

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 A. Yes. MICE is an advanced imputation technique that iteratively models each feature with missing values as a function of the other features using chained regression equations. It fills in missing values without removing any rows or columns, so the dimensionality of the feature set is…

Design and prepare a machine learning solution

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: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(44 responses)
  • A
    73% (32)
  • B
    27% (12)

Explanation

MICE is an advanced imputation technique that iteratively models each feature with missing values as a function of the other features using chained regression equations. It fills in missing values without removing any rows or columns, so the dimensionality of the feature set is fully preserved. It also accounts for uncertainty by generating multiple plausible imputed values, making it more statistically valid than simple substitution methods. Because it retains all rows and all columns, it allows analysis of the full dataset while meeting the dimensionality requirement.

Topics

#Missing data#Data imputation#MICE#Data cleaning

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