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Answer: Remove entire row
The question specifically mentions 'null rows' (plural), indicating entire rows where all values are missing. In Azure ML Studio's Clean Missing Data module, the 'Remove entire row' parameter is designed to handle cases where complete rows contain null/missing values. This approach is optimal because: 1) It directly addresses the problem of entire missing rows, 2) It prevents introducing bias from imputation methods when entire data records are absent, and 3) The community discussion shows strong consensus (100% of answers and multiple upvoted comments) supporting this choice. Other options like replacing with mean/mode or custom values are unsuitable for entire missing rows as they would create artificial data points, while removing the entire column would eliminate potentially valuable features unnecessarily.
Author: LeetQuiz Editorial Team
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You are creating a machine learning model with a dataset containing null rows. You plan to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing values.
Which parameter should you configure?
A
Replace with mean
B
Remove entire column
C
Remove entire row
D
Hot Deck
E
Custom substitution value
F
Replace with mode
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