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Answer: Create Scatterplot, Summarize Data, Build Counting Transform
The question requires three modules to visually identify and quantify outliers in the Age column before removal. Option A (Create Scatterplot) allows visual identification of outliers through plotting. Option B (Summarize Data) provides statistical measures (mean, median, standard deviation, quartiles) to quantify outliers. Option E (Build Counting Transform) creates frequency distributions to identify outliers with low frequency. While Option C (Clip Values) is for outlier removal, it is not for identification/quantification, and the community discussion (e.g., comments with upvotes favoring A, B, E) supports this, noting that Clip Values addresses removal, not the prerequisite steps. Options D and E are irrelevant, with D for discrete value replacement and E already selected for frequency analysis.
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You need to visually identify and quantify outliers in the Age column before removing them. Which three Azure Machine Learning Studio modules should you use in sequence? To answer, select the appropriate modules from the list of options.
A
Create Scatterplot
B
Summarize Data
C
Clip Values
D
Replace Discrete Values
E
Build Counting Transform