MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is building a regression model to predict house prices. The dataset includes a feature 'zip_code' with 1,000 unique values. What is the best way to handle this categorical feature in the exploratory data analysis phase?
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
✓
Apply target encoding using the mean house price per zip code
Target encoding (option B) is the best approach for high-cardinality categorical features like zip_code. It captures the relationship between the category and the target variable (house price) without creating an excessive number of dummy variables. One-hot encoding (option A) would create 1,000 columns, leading to high dimensionality and sparsity. Label encoding (option D) implies an ordinal relationship, which does not exist for zip codes. Frequency encoding (option C) may not capture price variation well because two zip codes with the same frequency could have very different average prices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
One-hot encode the zip_code feature
Why it's wrong here
One-hot encoding would create too many dummy variables (1,000 columns), leading to high dimensionality and sparsity, which is not ideal for a regression model.
- ✓
Apply target encoding using the mean house price per zip code
Why this is correct
Target encoding uses the mean house price per zip code, effectively capturing the relationship between zip code and the target while keeping dimensionality low.
- ✗
Replace zip_code with the frequency of each zip code in the dataset
Why it's wrong here
Frequency encoding replaces categories with their occurrence counts, which may not correlate well with house prices and can lose information about price differences.
- ✗
Use label encoding: assign each zip code a unique integer
Why it's wrong here
Label encoding assigns arbitrary integer values, implying an ordinal relationship among zip codes, which is not meaningful and can mislead the model.
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