MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is performing exploratory data analysis on a dataset containing customer transactions. They notice that the target variable is highly imbalanced: 99% of samples belong to class 0 and 1% to class 1. Which technique should they use to address this imbalance 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
✓
Apply SMOTE to generate synthetic samples for the minority class.
SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which helps balance the dataset and improves model performance on the minority class without losing information from the majority class. Option A is wrong: training on raw data without addressing imbalance will cause the model to be biased toward the majority class and perform poorly on the minority class. Option C is wrong: accuracy is not a suitable evaluation metric for imbalanced datasets because a model that always predicts the majority class will achieve 99% accuracy, masking poor performance on the minority class; instead, metrics like precision, recall, F1-score, or AUC should be used. Option D is wrong: under-sampling the majority class to match the minority class size discards a large amount of data, potentially losing valuable patterns and reducing model performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train the model on the raw data without any modification.
Why it's wrong here
Without addressing imbalance, the model will likely perform poorly on the minority class.
- ✓
Apply SMOTE to generate synthetic samples for the minority class.
Why this is correct
SMOTE creates synthetic minority samples, helping balance the dataset.
- ✗
Use accuracy as the evaluation metric and train on the raw data.
Why it's wrong here
Accuracy is misleading for imbalanced data; precision-recall or AUC is better.
- ✗
Under-sample the majority class to match the minority class size.
Why it's wrong here
Under-sampling may discard valuable majority class information.
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