MLS-C01 Exploratory Data Analysis Practice Question
During exploratory data analysis, a data scientist notices that the target variable is highly imbalanced. Which technique should be used to address this issue before training a classification model?
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
✓
Use SMOTE to generate synthetic samples for the minority class
SMOTE (Synthetic Minority Over-sampling Technique) is a popular method for handling imbalanced datasets by generating synthetic samples for the minority class. Option A (PCA) is wrong because dimensionality reduction does not address class imbalance. Option B (removing outliers) is wrong because it may worsen imbalance and is not a standard technique for imbalance. Option C (cross-validation) is a model evaluation technique, not a solution for imbalance. Option D (feature scaling) does not affect class distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply PCA to reduce dimensionality
Why it's wrong here
PCA does not solve class imbalance.
- ✗
Remove outliers from the majority class
Why it's wrong here
Removing outliers does not balance the classes; it may reduce data quality.
- ✗
Use cross-validation to evaluate the model
Why it's wrong here
Cross-validation evaluates model performance but does not fix imbalance.
- ✗
Apply feature scaling to all features
Why it's wrong here
Feature scaling does not address class imbalance.
- ✓
Use SMOTE to generate synthetic samples for the minority class
Why this is correct
SMOTE is a standard technique for imbalanced classification.
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