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MLA-C01 Practice Question: A machine learning team is preparing data for a…
A machine learning team is preparing data for a binary classification model. The target variable has a severe class imbalance (95% negative, 5% positive). The team wants to train a model that maximizes recall on the positive class while keeping training time manageable. Which approach should they use?
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 SMOTE to generate synthetic samples for the positive class
SMOTE (Synthetic Minority Oversampling Technique) creates synthetic samples of the minority class, increasing its representation without simply duplicating data. This often improves recall without drastically increasing training time compared to other methods.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use class weights inversely proportional to class frequencies
Why it's wrong here
Class weighting addresses imbalance without altering the dataset, but it may not maximize recall as effectively as resampling.
- ✓
Use SMOTE to generate synthetic samples for the positive class
Why this is correct
SMOTE creates new synthetic minority samples by interpolating between existing ones, balancing the dataset and improving recall.
- ✗
Oversample the positive class by simply duplicating existing records
Why it's wrong here
Simple oversampling can lead to overfitting and does not add new information.
- ✗
Undersample the majority class to match the minority class size
Why it's wrong here
Undersampling discards many samples, losing valuable information and potentially degrading model performance.
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