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MLA-C01 Practice Question: A data scientist is preparing a dataset for…
A data scientist is preparing a dataset for binary classification. The dataset has a target variable with 90% of samples belonging to class 0 and 10% to class 1. Which data splitting strategy should the scientist use to ensure that the 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
Stratified sampling ensures that each class is proportionally represented in the splits. Random splitting may not preserve the ratio; k-fold CV and time-series split are not appropriate for this requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Time-series split
Why it's wrong here
Used for temporal data, not for maintaining class proportions.
- ✗
Simple random split
Why it's wrong here
May produce splits with different class proportions due to randomness.
- ✗
k-fold cross-validation
Why it's wrong here
k-fold CV is a model evaluation technique, not a single train-test split.
- ✓
Stratified sampling
Why this is correct
Preserves class proportions in each split by sampling within each class.
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