SMOTE: Synthetic Minority Oversampling Technique
A healthcare analytics team is building a classification model to predict patient readmission within 30 days. The dataset contains 10,000 records with 30 features, including demographics, vital signs, lab results, and medication history. The target variable is imbalanced: 85% no readmission, 15% readmission. The team used logistic regression with default settings and achieved an accuracy of 85%, but the model predicted 'no readmission' for all patients. The lead analyst suspects the model is not learning due to class imbalance. The team has time to implement one corrective action before the next model review. Which action should the team take?
Quick Answer
The correct action is to apply SMOTE, the Synthetic Minority Oversampling Technique, to oversample the readmission class. SMOTE directly addresses class imbalance by generating synthetic examples of the minority class rather than simply duplicating existing records, which forces the logistic regression model to learn meaningful decision boundaries instead of defaulting to the majority class. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding that accuracy is a misleading metric when classes are imbalanced—an 85% accuracy here is worthless because the model never predicts readmission. A common trap is choosing random undersampling of the majority class, but that discards valuable data, whereas SMOTE preserves information while balancing the dataset. Memory tip: SMOTE stands for “Synthetic Minority Oversampling Technique”—think of it as “smoothing” the minority class with new, realistic data points to help the model see the full picture.
⚠ Common exam trap
Many candidates choose accuracy as a metric (Option C) because it seems intuitive, but in imbalanced datasets, accuracy is misleading and does not reflect model performance for 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
✓
Apply SMOTE to oversample the readmission class
SMOTE (Synthetic Minority Oversampling Technique) directly addresses the class imbalance by generating synthetic samples for the minority class (readmission). This forces the logistic regression model to learn decision boundaries that separate the two classes, rather than defaulting to the majority class prediction. With 85% majority and 15% minority, accuracy alone is misleading, and SMOTE is a proven technique to improve recall for 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.
- ✗
Remove features with low variance to reduce noise
Why it's wrong here
Low-variance feature removal addresses noise and dimensionality, not the 85/15 class imbalance that causes the all-negative predictions. It is tempting because feature selection is a standard preprocessing step, and it would be the right action if irrelevant or near-constant features were degrading model performance.
- ✓
Apply SMOTE to oversample the readmission class
Why this is correct
SMOTE synthesises minority-class readmission examples, rebalancing the 85/15 split so logistic regression no longer converges on the majority class. This directly counters the imbalance causing the all-negative predictions, addressing the stated constraint within one corrective action.
- ✗
Use accuracy as the evaluation metric to monitor improvement
Why it's wrong here
Accuracy is exactly the metric already misleading the team: predicting the majority class alone yields 85%, so monitoring it cannot reveal improved minority-class detection. It is tempting because accuracy is the default scoring output, and it would be appropriate for a balanced dataset where classes occur at similar rates.
- ✗
Switch to a random forest model with default settings
Why it's wrong here
Random forest with default settings still optimises overall impurity, so the 85% majority class dominates and predictions stay biased toward 'no readmission'. It is tempting because ensemble methods capture non-linear feature interactions, which would help if the problem were model capacity rather than class imbalance.
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Same concept, more angles
2 more ways this is tested on DA0-002
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 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?
medium- A.Use the dataset as is because logistic regression handles imbalance
- B.Remove most of the non-readmitted patients to balance the dataset
- C.Use accuracy as the evaluation metric
- ✓ D.Apply oversampling techniques like SMOTE to the training set
Why D: 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.
Variation 2. A company is analyzing customer feedback sentiment. The dataset is highly imbalanced with 95% positive and 5% negative comments. Which technique should the analyst use to address class imbalance before modeling?
hard- A.Use accuracy as the evaluation metric
- B.Undersample the majority class
- C.Oversample the majority class
- ✓ D.Use SMOTE
Why D: SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic samples for the minority class (negative comments) by interpolating between existing minority instances, rather than simply duplicating them. This addresses the 95:5 imbalance without the information loss of undersampling or the overfitting risk of naive oversampling.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DA0-002 practice question is part of Courseiva's free CompTIA 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 DA0-002 exam.