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MLA-C01 Practice Question: A data scientist is training a binary…
A data scientist is training a binary classification model using Amazon SageMaker. The dataset has a severe class imbalance (95% negative, 5% positive). The model achieves 99% accuracy but fails to identify positive cases correctly. Which action should the data scientist take to improve the model's ability to detect positive cases?
⚠ Common exam trap
It's easy for candidates to think oversampling (SMOTE) or changing the model type is the primary fix, but the exam tests understanding that evaluation metrics and threshold tuning are critical for imbalanced classification, not just data preprocessing.
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 the F1 score as the evaluation metric and adjust the classification threshold based on the precision-recall curve.
In a severely imbalanced dataset (95% negative, 5% positive), accuracy is misleading. The F1 score balances precision and recall, and adjusting the classification threshold based on the precision-recall curve allows the model to prioritize recall for the minority class, directly improving detection of positive cases. This approach is recommended in SageMaker when using built-in algorithms or custom models with imbalanced 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.
- ✗
Switch to a logistic regression model with balanced class weights.
Why it's wrong here
Model choice alone does not guarantee improved recall.
- ✗
Use accuracy as the evaluation metric and retrain the model.
Why it's wrong here
Accuracy is misleading for imbalanced classes.
- ✗
Apply SMOTE (Synthetic Minority Over-sampling Technique) to the training data.
Why it's wrong here
SMOTE is a valid approach but not the most direct action for improving detection of positive cases.
- ✓
Use the F1 score as the evaluation metric and adjust the classification threshold based on the precision-recall curve.
Why this is correct
F1 score and threshold tuning directly address the imbalance.
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