MLS-C01 Modeling Practice Question
A company is building a multiclass classification model using Amazon SageMaker. The dataset has 100 classes and is highly imbalanced. The model currently achieves high accuracy on the majority classes but poor performance on minority classes. Which technique should the data scientist use to improve minority class performance?
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 class weights to penalize misclassifications of minority classes
Class weights assign a higher penalty to misclassifications of minority classes, forcing the model to pay more attention to them and improving recall for minority classes. Option A is wrong because random oversampling with replacement can cause overfitting and does not incorporate minority class importance as directly as class weights. Option B is wrong because PCA is a dimensionality reduction technique and does not address class imbalance. Option D is wrong because removing samples from the majority class (undersampling) can discard useful information and is often less effective than using class weights.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply random oversampling with replacement
Why it's wrong here
Random oversampling with replacement can lead to overfitting and does not directly address the need to better classify minority classes compared to using class weights.
- ✗
Apply principal component analysis (PCA)
Why it's wrong here
Principal component analysis (PCA) is a dimensionality reduction technique and does not address class imbalance.
- ✓
Use class weights to penalize misclassifications of minority classes
Why this is correct
Class weights penalize errors on minority classes more, improving their recall.
- ✗
Remove samples from majority classes
Why it's wrong here
Removing samples from majority classes (undersampling) can discard potentially useful data and may reduce overall model performance.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.