MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset of customer reviews for a retail company. The dataset contains text reviews, star ratings (1-5), and customer metadata. The scientist wants to perform sentiment analysis to classify reviews as positive or negative. During EDA, the scientist uses Amazon SageMaker Data Wrangler to visualize the distribution of star ratings and notices that 90% of reviews are 4 or 5 stars, while only 2% are 1 star. The scientist is concerned about class imbalance. Which approach should the scientist take to address the imbalance 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
✓
Use the F1-score as the evaluation metric to account for imbalance.
The F1-score balances precision and recall, making it a suitable metric for imbalanced datasets where accuracy would be misleading due to the majority class dominating. Option A (downsampling) is not ideal because it discards potentially useful data from the majority class. Option B (random oversampling) can lead to overfitting by duplicating minority class instances, and is not a guaranteed solution. Option C is incorrect because accuracy is not reliable when classes are imbalanced, as high accuracy can be achieved by simply predicting the majority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Downsample the majority class to create a balanced dataset.
Why it's wrong here
Downsampling the majority class would discard 90% of the 4- and 5-star reviews, wasting the rich textual signal in the most common sentiment and severely reducing the effective training set size. This approach is tempting because it directly balances class proportions, which is a standard remedy for binary classification when the minority class is underrepresented and the majority class contains redundant or noisy data.
- ✗
Use random oversampling of the minority class to balance the dataset.
Why it's wrong here
Oversampling can cause overfitting; not necessarily the best first step.
- ✗
Use accuracy as the evaluation metric since it is simple.
Why it's wrong here
Accuracy is misleading when classes are imbalanced.
- ✓
Use the F1-score as the evaluation metric to account for imbalance.
Why this is correct
F1-score balances precision and recall, appropriate for imbalanced classes.
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