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MLA-C01 Practice Question: A machine learning practitioner is building a…
A machine learning practitioner is building a binary classifier with severe class imbalance (1:1000). They want to use SMOTE for oversampling. What is a potential drawback of applying SMOTE on the entire dataset before splitting into training and test sets?
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
✓
It causes data leakage, making validation metrics overly optimistic
Applying SMOTE before splitting causes data leakage because synthetic samples are generated using information from both training and test sets, leading to overly optimistic performance estimates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SMOTE increases the risk of overfitting to the minority class
Why it's wrong here
Overfitting is a risk, but the primary issue is leakage.
- ✗
SMOTE cannot be applied to categorical features
Why it's wrong here
SMOTE can be applied to numerical features; categorical features need separate handling.
- ✓
It causes data leakage, making validation metrics overly optimistic
Why this is correct
Generating synthetic samples before splitting leaks information from the test set into the training set.
- ✗
SMOTE generates synthetic samples that may not be realistic
Why it's wrong here
Realism is a concern, but leakage is more critical.
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