DA0-002 Data Analysis Practice Question
A healthcare analytics team is analyzing patient readmission rates. They have a dataset with thousands of records including patient age, diagnosis, length of stay, number of prior admissions, and discharge date. The goal is to identify key factors influencing readmission and create a model to predict high-risk patients. The data is imbalanced: only 5% of patients are readmitted within 30 days. The team plans to use logistic regression. What is the most appropriate approach?
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
✓
Apply oversampling techniques like SMOTE to the training set
With imbalanced data, logistic regression can be biased toward the majority class. Oversampling the minority class (e.g., SMOTE) helps the model learn patterns for readmission. Using accuracy as a metric would be misleading. Removing majority samples discards valuable data. Using data as-is often fails to predict the minority 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.
- ✗
Use the dataset as is because logistic regression handles imbalance
Why it's wrong here
Logistic regression is sensitive to class imbalance and may predict the majority class only.
- ✗
Remove most of the non-readmitted patients to balance the dataset
Why it's wrong here
This discards useful data and reduces sample size, potentially losing information.
- ✗
Use accuracy as the evaluation metric
Why it's wrong here
Accuracy is misleading for imbalanced data; precision-recall or AUC is better.
- ✓
Apply oversampling techniques like SMOTE to the training set
Why this is correct
Oversampling balances the classes, improving model performance on the minority class.
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