Explanation:
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 data distribution plots. Option B (Summarize Data) provides statistical measures (mean, median, standard deviation, quartiles) to quantify outliers numerically. Option E (Build Counting Transform) creates frequency distributions to identify outliers with low occurrence rates. These three modules collectively address both visualization and quantification aspects. Option C (Clip Values) is for outlier treatment/removal, not identification/quantification, and Option D (Replace Discrete Values) is unrelated to outlier detection in continuous data like Age.
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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. NOTE: Each correct selection is worth one point.
A
Create Scatterplot
B
Summarize Data
C
Clip Values
D
Replace Discrete Values
E
Build Counting Transform
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