MLS-C01 Modeling Practice Question
Which TWO techniques are used to handle missing values in a dataset before training? (Choose 2.)
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
✓
Mean or median imputation.
Mean or median imputation is a common method to fill missing values with a central tendency measure. Option C is correct because removing rows or columns with missing values is a valid approach, especially when the missing data is minimal. Option B (min-max scaling) is for normalizing numerical features, not for missing values. Option D (one-hot encoding) is for converting categorical variables into numerical format. Option E (PCA) is for dimensionality reduction, not missing value handling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Mean or median imputation.
Why this is correct
Imputation replaces missing values with central tendency.
- ✗
Min-max scaling.
Why it's wrong here
Scaling does not handle missing values.
- ✓
Removing rows or columns with missing values.
Why this is correct
Deletion is a simple approach to handle missing data.
- ✗
One-hot encoding.
Why it's wrong here
Encoding is for categorical data, not missing values.
- ✗
Principal component analysis (PCA).
Why it's wrong here
PCA is for dimensionality reduction, not missing values.
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