MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist finds that a categorical feature 'city' has 500 unique values but only 10 cities account for 90% of the data. What is a recommended way to handle the rare categories?
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 a single 'Other' category.
Grouping rare categories into 'Other' reduces cardinality, avoids overfitting from high-dimensional sparse features, and retains the majority of data from the top 10 cities. Option B (label encoding) is not recommended as it imposes an arbitrary ordinal relationship that may mislead the model. Option C (one-hot encoding all 500 categories) would create 499 dummy features, leading to the curse of dimensionality and sparse data. Option D (dropping rows with rare categories) discards potentially valuable data and may introduce bias.
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 a single 'Other' category.
Why this is correct
Reduces cardinality and retains data.
- ✗
Apply label encoding to all categories.
Why it's wrong here
Label encoding may not handle rarity well.
- ✗
One-hot encode all 500 categories.
Why it's wrong here
Too many dummy variables.
- ✗
Drop all rows with rare categories.
Why it's wrong here
Loses data.
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