MLS-C01 Exploratory Data Analysis Practice Question
Which TWO approaches are appropriate for handling missing categorical data during exploratory data analysis? (Choose two.)
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
✓
Impute with the mode (most frequent) of the column.
Options B and C are correct. Imputing with the mode (B) is a simple and effective method for categorical data, as it preserves the most frequent category without introducing new values. Treating missing values as a separate 'Unknown' category (C) allows the model to capture potential patterns associated with missingness, which can be informative. Option A is incorrect because one-hot encoding is a technique for representing categorical variables, not for handling missing data; it requires the values to be known first. Option D is incorrect because dropping rows with missing values can result in significant data loss and may introduce bias, especially if missingness is not random. Option E is incorrect because mean imputation is suitable for numerical data, not categorical data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use one-hot encoding to represent missingness as a binary feature.
Why it's wrong here
One-hot encoding requires a value, not missing.
- ✓
Impute with the mode (most frequent) of the column.
Why this is correct
Mode is a simple imputation for categorical data.
- ✓
Treat missing values as a separate 'Unknown' category.
Why this is correct
This preserves missingness pattern.
- ✗
Drop all rows with missing values in that column.
Why it's wrong here
Dropping rows may discard valuable data.
- ✗
Impute missing values with the mean of the column.
Why it's wrong here
Mean is for numerical data, not categorical.
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