MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with high cardinality categorical features (e.g., user IDs with millions of unique values). They want to visualize the relationship between these categorical features and a continuous target variable. Which approach is most effective for EDA?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Group rare categories into an 'Other' category and use box plots
For high cardinality categorical features like user IDs with millions of unique values, directly visualizing all categories is infeasible. Grouping rare categories into an 'Other' category reduces cardinality, enabling effective comparison of the continuous target distribution across categories using box plots. This approach preserves meaningful information while avoiding clutter. Option B (one-hot encoding) creates an excessively wide feature set unsuitable for simple visualization. Option C (bar chart with all categories) would be overcrowded and unreadable. Option D (removing features) discards potentially valuable information. Option E (feature hashing) is more appropriate for modeling pipelines, not for intuitive EDA visualization. Thus, Option A is the most effective approach.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Group rare categories into an 'Other' category and use box plots
Why this is correct
Grouping reduces cardinality and box plots effectively show relationship with target.
- ✗
Apply one-hot encoding and use scatter plots
Why it's wrong here
One-hot encoding creates too many dimensions and scatter plots become unreadable.
- ✗
Use a bar chart with all categories on x-axis
Why it's wrong here
Bar chart with millions of categories is not interpretable.
- ✗
Remove the categorical features from analysis
Why it's wrong here
Removing features loses potential information.
- ✗
Apply feature hashing and visualize the hashed values
Why it's wrong here
Feature hashing is for modeling, not for interpretable EDA.
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