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MLA-C01 Practice Question: A machine learning engineer needs to split a…
A machine learning engineer needs to split a dataset for binary classification where the positive class represents only 2% of the data. Which data splitting strategy ensures that both training and test sets maintain the same class proportion as the original dataset?
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
✓
Stratified sampling based on the target variable
Stratified splitting preserves the original class distribution in each split, which is critical for imbalanced datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Stratified sampling based on the target variable
Why this is correct
Stratified sampling ensures each fold retains the same class proportion as the full dataset.
- ✗
Time-series split respecting the timestamp order
Why it's wrong here
Time-series split is for temporal data, not class imbalance.
- ✗
Simple random split with a 80/20 ratio
Why it's wrong here
Random split may result in test sets with zero positive samples due to the rare class.
- ✗
K-fold cross-validation without stratification
Why it's wrong here
Without stratification, some folds may have no positive samples.
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