Answer-first summary for fast verification
Answer: No
The solution does not meet the goal because removing columns that contain missing values reduces the dimensionality of the feature set, which directly contradicts the requirement to preserve dimensionality. The goal specifies cleaning missing values without affecting feature set dimensionality and analyzing a complete dataset with all values. Removing columns eliminates features entirely, reducing the number of columns and thus the dimensionality. Community discussion strongly supports this with 100% consensus on answer B (No), with comments noting that removing columns affects dimensions and that entire columns should not be removed for single missing data points. Appropriate methods for preserving dimensionality while handling missing values include imputation techniques like mean/median imputation or MICE, not column removal.
Author: LeetQuiz Editorial Team
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You are working with a numerical dataset that has missing values in multiple columns. You must clean the missing data using a method that preserves the dimensionality of the feature set. The goal is to perform an analysis on the complete dataset, including all values.
Proposed Solution: Remove any column that contains at least one missing value.
Does this solution achieve the goal?
A
Yes
B
No