MLS-C01 Modeling Practice Question
Which TWO of the following are valid approaches to handle missing values in a dataset for a machine learning model?
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 missing values with the mean of the column
Removing rows with missing values is a valid approach (listwise deletion). Imputing with the mean is also valid. Using a neural network to predict missing values is possible but not standard. Standardization does not handle missing values. One-hot encoding is for categorical variables.
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 a neural network to predict missing values
Why it's wrong here
Using a neural network to predict missing values introduces model complexity and risk of overfitting, especially when the missingness mechanism is non-random or the dataset is small; the question requires straightforward, assumption-free imputation methods such as mean/median substitution or deletion, which do not require training a separate predictive model. It is tempting because neural networks excel at learning complex patterns from data, and in a large, high-dimensional dataset with ample labelled examples, they could serve as a valid imputation technique for missing features.
- ✓
Impute missing values with the mean of the column
Why this is correct
Mean imputation is a standard technique for numerical features.
- ✓
Remove rows with missing values
Why this is correct
Deleting rows with missing values is a common approach when missing data is minimal.
- ✗
Standardize the features to handle missing values
Why it's wrong here
Standardization does not address missing values; it only rescales features.
- ✗
Apply one-hot encoding to convert missing values
Why it's wrong here
One-hot encoding is for categorical variables, not for handling missing values.
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