MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is using Amazon SageMaker to train a model. The training dataset contains missing values in several features. The data scientist wants to impute missing values using the median of each feature. Which approach is most appropriate?
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
✓
Compute the median of each feature on the training set only, then impute both training and test sets using that median
Computing the median on the training set only avoids data leakage, and applying that median to both training and test sets ensures consistent imputation without using test set information. Option A is incorrect because dropping rows with missing values discards potentially useful data and is not imputation. Option B is incorrect because computing the median on the entire dataset before splitting introduces data leakage, as the test set influences the imputation values. Option C is incorrect because imputing with zero is arbitrary and does not use the median; also, doing it before splitting would use the entire dataset, causing leakage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Drop all rows that contain missing values
Why it's wrong here
Dropping rows discards potentially useful data and is not imputation; it may lead to loss of information and is not a preferred method for handling missing values.
- ✗
Compute the median on the entire dataset, then split into training and test sets
Why it's wrong here
Computing the median on the entire dataset before splitting introduces data leakage because the test set influences the imputation values, violating the principle of not using test set information.
- ✗
Impute missing values with zero for all features before splitting
Why it's wrong here
Imputing with zero is arbitrary and does not use the median; also, doing it before splitting uses the entire dataset, causing data leakage.
- ✓
Compute the median of each feature on the training set only, then impute both training and test sets using that median
Why this is correct
Computing the median on the training set only avoids data leakage, and applying that median to both training and test sets ensures consistent imputation without using test set information.
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