MLS-C01 Modeling Practice Question
Which TWO of the following are valid methods for handling missing values in a dataset before training a machine learning model?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that decision tree algorithms inherently handle missing values without any preprocessing, but in practice, they require explicit handling (e.g., surrogate splits) and do not automatically resolve missing data for all model training scenarios.
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
✓
Remove rows that contain missing values
Removing rows with missing values (listwise deletion) is a straightforward and valid method when the missing data is random and the dataset is large enough that the loss of rows does not significantly reduce statistical power or introduce bias. This approach ensures that only complete cases are used for training, avoiding the need to estimate missing values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Remove rows that contain missing values
Why this is correct
If the proportion of missing data is small, dropping rows is a valid option.
- ✗
Use a decision tree algorithm that handles missing values internally
Why it's wrong here
This is an algorithmic approach, not a preprocessing step for handling missing values before training.
- ✗
Increase the number of trees in a random forest
Why it's wrong here
Increasing trees does not handle missing values; it affects model performance but not missing data.
- ✗
Replace missing values with zero
Why it's wrong here
Setting missing values to zero is generally not recommended unless zero has a special meaning.
- ✓
Impute missing values with the mean of the column
Why this is correct
Mean imputation is a simple and common method for numerical features.
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