MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). The dataset has 10 million rows. The goal is to train a binary classifier. Which technique should be applied during exploratory data analysis to best address the imbalance?
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
✓
Random undersampling of the majority class
Random undersampling of the majority class is a practical approach for large datasets like 10M rows to reduce class imbalance during EDA. Option A (assign higher class weights) is a modeling technique applied during training, not during EDA. Option C (SMOTE) generates synthetic samples but can be computationally expensive for 10M rows. Option D (collect more data) does not guarantee a balanced distribution and may not be feasible.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Assign higher class weights to the minority class
Why it's wrong here
This is done during model training, not EDA.
- ✓
Random undersampling of the majority class
Why this is correct
Feasible for large datasets and can balance classes.
- ✗
Synthetic Minority Oversampling Technique (SMOTE)
Why it's wrong here
Computationally expensive for large datasets.
- ✗
Collect more data for the minority class
Why it's wrong here
Not always possible and not a guaranteed fix.
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