MLA-C01 Data Preparation for Machine Learning Practice Question

A data scientist is preparing a dataset for a linear regression model. The dataset has a few missing values in a numerical feature with a normal distribution and no outliers. Which imputation method is most appropriate?

⚠ Common exam trap

Many candidates confuse the median with the mean for normal distributions, but the median is actually less efficient and can lead to biased variance estimates, while the mean is the maximum likelihood estimator for normally distributed data with no outliers.

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 mean

For a numerical feature with a normal distribution and no outliers, the mean is the most appropriate imputation method because it preserves the central tendency of the data without introducing bias. In linear regression, mean imputation maintains the expected value of the feature, which is critical for unbiased coefficient estimates when data are missing completely at random (MCAR).

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Impute with mode
Why it's wrong here
Mode is for categorical variables, not numerical.
✓
Impute with mean
Why this is correct
Mean is appropriate for normally distributed numerical data without outliers.
✗
Impute with median
Why it's wrong here
Median is robust to outliers, but not necessary here and may introduce bias.
✗
Drop rows with missing values
Why it's wrong here
Dropping rows reduces dataset size and may bias the model.

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MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

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