MLS-C01 Exploratory Data Analysis Practice Question
During exploratory data analysis, a data scientist notices that a categorical feature 'city' has over 1,000 unique values. The dataset has 10,000 rows. Which technique should the scientist consider to reduce the cardinality of this feature?
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 low-frequency categories into a single 'other' category.
Grouping rare categories into an 'other' bucket is a common technique to reduce cardinality. Option A (label encoding) assigns numeric labels but still has 1000 unique values. Option B (grouping into 'other') reduces cardinality. Option C (one-hot encoding) would create too many columns. Option D (frequency encoding) replaces categories with frequency but still has 1000 values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply label encoding to assign numeric labels.
Why it's wrong here
Label encoding reduces cardinality but preserves the number of unique values.
- ✓
Group low-frequency categories into a single 'other' category.
Why this is correct
Grouping rare categories reduces cardinality effectively.
- ✗
Apply one-hot encoding to all categories.
Why it's wrong here
One-hot encoding would create 1000+ columns, which is not practical.
- ✗
Apply frequency encoding to replace each category with its frequency.
Why it's wrong here
Frequency encoding still has 1000 unique values (the frequencies).
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