Question 1,309 of 1,672
SMOTE and Class Weights for Imbalanced Classification
A data scientist is building a binary classifier to predict customer churn. The dataset is highly imbalanced (5% churn). Which TWO techniques can help improve the model's ability to detect churn?
Quick Answer
The correct techniques are SMOTE and class weights, as both directly address the imbalance in your training data. SMOTE, or Synthetic Minority Over-sampling Technique, generates synthetic examples for the minority class rather than simply duplicating existing ones, which helps the model learn more robust decision boundaries. Class weights, on the other hand, adjust the loss function to assign a higher penalty to misclassifications of the minority class, forcing the model to pay more attention to the rare churn cases. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of practical data imbalance strategies beyond simple resampling—a common trap is choosing downsampling the majority class, which discards potentially valuable data, or relying on accuracy as a metric, which is misleading for imbalanced datasets. A useful memory tip: think of SMOTE as “synthesizing” new minority data and class weights as “weighting” the error—both boost the minority signal without losing majority information.
⚠ Common exam trap
Many candidates assume downsampling (Option A) is always beneficial for imbalance, but it can discard critical majority class patterns, whereas SMOTE and class weights (Options B and C) preserve data while directly targeting the minority class.
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 Synthetic Minority Over-sampling Technique (SMOTE)
SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, effectively balancing the dataset and providing the model with more diverse churn examples to learn from. This directly addresses the class imbalance problem without losing information from 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 balance the dataset
Why it's wrong here
Downsampling discards data, which can be suboptimal.
- ✓
Use Synthetic Minority Over-sampling Technique (SMOTE)
Why this is correct
SMOTE generates synthetic samples for the minority class.
- ✓
Use class weights in the loss function to penalize misclassifications of the minority class
Why this is correct
Class weights focus the model on the minority class.
- ✗
Use accuracy as the evaluation metric
Why it's wrong here
Accuracy is misleading for imbalanced data.
- ✗
Increase the model complexity by adding more layers
Why it's wrong here
Increased complexity may lead to overfitting.
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Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (positive class). Which TWO techniques should be used to address the class imbalance? (Choose 2.)
easy- A.Use a higher learning rate during training
- B.Use L1 regularization on the model
- ✓ C.Use random undersampling of the majority class
- ✓ D.Use SMOTE to generate synthetic samples for the minority class
- E.Use principal component analysis (PCA) to reduce dimensionality
Why C: Random undersampling of the majority class (Option C) reduces the number of non-churner samples to balance the dataset, preventing the model from being biased toward the majority class. SMOTE (Option D) generates synthetic samples for the minority class by interpolating between existing minority instances, which increases the representation of churners without simply duplicating data. Both techniques directly address class imbalance by modifying the training data distribution.
Variation 2. A data scientist is building a binary classification model to predict loan default. The dataset is highly imbalanced (5% default, 95% non-default). Which TWO techniques should the data scientist use to address the class imbalance?
hard- A.Undersample the majority class
- B.Use RMSE as the evaluation metric
- ✓ C.Oversample the minority class using SMOTE
- D.Use accuracy as the evaluation metric
- ✓ E.Use class weights in the loss function
Why C: Oversampling the minority class using SMOTE (Synthetic Minority Oversampling Technique) is correct because it generates synthetic samples for the minority class by interpolating between existing minority instances, rather than simply duplicating them. This helps balance the dataset without introducing exact copies, which can reduce overfitting and improve the model's ability to generalize to the minority class.
Last reviewed: Jul 4, 2026
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