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Answer: Remove entire row
The question specifically mentions 'null rows' (plural), indicating entire rows with null values across multiple columns. In Azure ML Studio's Clean Missing Data module, the 'Remove entire row' parameter is the most appropriate choice when dealing with completely null rows, as these rows contain no useful information for model training and would negatively impact model performance. The community discussion shows strong consensus (100% of answers and multiple upvoted comments) supporting option C, with reasoning that null rows provide no value and should be removed entirely. Other options like replacing with mean/mode or custom values are unsuitable for completely null rows since there's no valid data to base imputation on, and removing entire columns would discard potentially valuable data from other rows.
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You are creating a machine learning model and have a dataset containing null rows. You need 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