Question 582 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 1% of the data. The model needs to maximize recall while keeping precision above 0.7. Which sampling strategy should the data scientist 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
✓
SMOTE from imbalanced-learn to generate synthetic samples for the minority class.
(SMOTE) is correct because it generates synthetic samples for the minority class, which can improve recall without discarding data, and synthetic samples help maintain precision above 0.7 by providing more balanced training. Option A (NearMiss) undersamples majority samples based on distance, potentially discarding important data and reducing recall. Option C (RandomUnderSampler) may lose too many majority samples, harming recall and precision. Option D (TomekLinks) only removes overlapping samples, which does not sufficiently address imbalance. Option E (RandomOverSampler) duplicates minority samples, which can cause overfitting and reduce precision on unseen data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
NearMiss from imbalanced-learn to undersample the majority class based on distance to minority samples.
Why it's wrong here
NearMiss focuses on hard-to-classify samples, which may reduce recall for the minority class.
- ✓
SMOTE from imbalanced-learn to generate synthetic samples for the minority class.
Why this is correct
SMOTE creates synthetic samples, balancing the dataset and improving recall while preserving precision.
- ✗
RandomUnderSampler from imbalanced-learn to undersample the majority class.
Why it's wrong here
Undersampling can lose important majority class information, reducing precision.
- ✗
TomekLinks from imbalanced-learn to remove overlapping samples.
Why it's wrong here
TomekLinks only removes noisy samples and does not significantly change class balance.
- ✗
RandomOverSampler from imbalanced-learn to oversample the minority class.
Why it's wrong here
Oversampling duplicates minority samples, which can lead to overfitting and may not improve recall sufficienty.
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Last reviewed: Jun 20, 2026
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