You are creating a machine learning model. You need to identify outliers data. Which two visualizations can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is…

The correct answer is A. box plot B. Scatter plot. Box plots explicitly visualize outliers: data points that fall beyond the upper or lower whiskers (typically 1.5× the interquartile range) are plotted as individual dots, making outlier identification straightforward. Scatter plots reveal outliers as points that lie far from…

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Question

You are creating a machine learning model. You need to identify outliers data. Which two visualizations can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Options

Abox plot
BScatter plot
Crandom forest diagram
DVenn diagram
EROC curve

How the community answered

(17 responses)

A
94% (16)
D
6% (1)

Explanation

Box plots explicitly visualize outliers: data points that fall beyond the upper or lower whiskers (typically 1.5× the interquartile range) are plotted as individual dots, making outlier identification straightforward. Scatter plots reveal outliers as points that lie far from the main cluster or trend line in the feature space. Random forest diagrams depict model tree structures, Venn diagrams illustrate set overlaps, and ROC curves evaluate binary classifier performance at different thresholds - none of these are used to detect outlier data points.

Topics

#Data Visualization#Outlier Detection#Exploratory Data Analysis (EDA)#Data Exploration

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