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Answer: Convert to Indicator Values
The statement is incorrect because the Clean Missing Data module is designed to handle missing values in datasets, not to transform categorical features into binary indicators. The correct module for converting categorical features into binary indicators (one-hot encoding) is 'Convert to Indicator Values'. This is supported by the community discussion where 93% selected option B, with multiple comments confirming that 'Convert to Indicator Values' is equivalent to one-hot encoding for categorical columns. Microsoft documentation also confirms this module's purpose is to convert categorical values into binary indicator columns for machine learning features.
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You need to evaluate the accuracy of the following statement: To transform a categorical feature into a binary indicator, you should use the Clean Missing Data module.
Select No adjustment required if the statement is correct. If the statement is incorrect, choose the accurate alternative.
A
No adjustment required.
B
Convert to Indicator Values
C
Apply SQL Transformation
D
Group Categorical Values
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