MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with 10 million rows and 50 columns. The target variable is highly imbalanced (99% negative, 1% positive). Which approach is most appropriate for exploratory data analysis before modeling?
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
✓
Take a stratified sample that preserves the 99:1 ratio.
C is correct because stratified sampling preserves the original class proportion (99:1) in the sample, which is important for exploratory data analysis on imbalanced data without artificially altering the distribution. Option A (remove all negatives) loses all negative data, preventing analysis of majority class patterns. Option B (random sample) may result in insufficient positive examples due to imbalance. Option D (up-sample minority) would change the distribution and could lead to misleading visualizations and statistics during EDA.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all negative examples and analyze only the positive ones.
Why it's wrong here
Removing negatives discards most of the data.
- ✗
Take a random sample of 100,000 rows from the entire dataset.
Why it's wrong here
Random sampling may not capture enough positive examples.
- ✓
Take a stratified sample that preserves the 99:1 ratio.
Why this is correct
Stratified sampling ensures representation of both classes.
- ✗
Up-sample the minority class to balance the dataset before analysis.
Why it's wrong here
Up-sampling before EDA can introduce artificial patterns.
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