Answer-first summary for fast verification
Answer: Yes
The recommendation to use the Custom substitution value option in the Clean Missing Data module satisfies the requirements because this option allows specifying a placeholder value (e.g., 0 for numeric columns or 'NA' for text columns) to replace all missing values, provided the substitution is compatible with each column's data type. The community discussion supports this, with 79% selecting 'Yes' (option A) and referencing Microsoft documentation confirming the option's applicability. While some comments (e.g., advocating for row removal or imputation methods) suggest alternatives, the question specifically asks if the Custom substitution value meets the requirement to handle null/missing values, and it does, as it is a valid and flexible method within the module. The key is that the substitution value must align with data types, but the option itself is suitable for the task.
Author: LeetQuiz Editorial Team
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You are creating a machine learning model using a dataset that contains null and missing values. You plan to use the Clean Missing Data module in Azure Machine Learning Studio to handle these values.
Recommendation: Use the Custom substitution value option.
Does this recommendation meet the requirements?
A
Yes
B
No
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