MLS-C01 Class Imbalance Practice Question
A data scientist is working with a dataset containing text reviews. The goal is to build a sentiment analysis model. Which EDA step is most critical before feature extraction?
⚠ Common exam trap
A common pitfall is jumping directly into text preprocessing (stop word removal, tokenization) without first examining the label distribution, which can lead to biased models and misleading accuracy metrics.
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
✓
Checking the distribution of sentiment labels
Checking the distribution of sentiment labels is critical before feature extraction because it reveals class imbalance, which can bias the model towards the majority class and affect evaluation metrics. This EDA step enables informed decisions about resampling or weighting techniques. Option A (vocabulary size) is not a critical first step; option B (word cloud) is a visualization tool, not essential; option C (removing stop words) is a preprocessing step, not part of 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.
- ✗
Calculating the vocabulary size
Why it's wrong here
Vocabulary size is not critical for the initial EDA.
- ✗
Creating a word cloud
Why it's wrong here
Word cloud is a visualization, not a critical EDA step.
- ✗
Removing stop words
Why it's wrong here
Stop word removal is preprocessing, not EDA.
- ✓
Checking the distribution of sentiment labels
Why this is correct
Class imbalance can significantly impact model performance.
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