- A
Precision
Why wrong: Precision measures accuracy of positive predictions, but the priority is catching actual churners.
- B
F1-score
Why wrong: F1-score is a harmonic mean of precision and recall, but recall alone is more appropriate here.
- C
Recall (TPR)
Recall focuses on identifying positive cases, which is the main objective.
- D
Specificity
Why wrong: Specificity measures true negative rate, not relevant for identifying churners.
Question 744 of 1,000
Choosing the Right Metric for Imbalanced Classification
This AI0-001 practice question tests your understanding of machine learning and deep learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 80% non-churn and 20% churn. The model achieves 95% accuracy but fails to identify churners correctly. Which metric should the scientist focus on to evaluate model performance properly?
Quick Answer
The answer is recall (true positive rate). Recall is the correct evaluation metric for imbalanced classification because it measures the model’s ability to correctly identify all actual positive cases—in this scenario, the churners—regardless of how many non-churners are misclassified. When a dataset has 80% non-churn and 20% churn, a model can achieve 95% accuracy by simply predicting the majority class, but that fails the business goal of catching churners. On the CompTIA AI+ AI0-001 exam, this question tests your understanding that accuracy is misleading for imbalanced data, and that recall prioritizes sensitivity to the minority class. A common trap is choosing F1-score, which balances precision and recall, but recall is more directly needed when the cost of missing churners is high. Memory tip: “Recall the rare ones”—if you need to catch every positive, recall is your go-to metric.
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
✓
Recall (TPR)
Recall (True Positive Rate) measures the proportion of actual churners correctly identified by the model. With 80% non-churn and 20% churn, a model can achieve 95% accuracy by simply predicting the majority class (non-churn) for all samples, resulting in zero true positives for churn. Recall directly exposes this failure by quantifying how many churners are captured, making it the critical metric for imbalanced classification problems.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precision
Why it's wrong here
Precision measures accuracy of positive predictions, but the priority is catching actual churners.
- ✗
F1-score
Why it's wrong here
F1-score is a harmonic mean of precision and recall, but recall alone is more appropriate here.
- ✓
Recall (TPR)
Why this is correct
Recall focuses on identifying positive cases, which is the main objective.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Specificity
Why it's wrong here
Specificity measures true negative rate, not relevant for identifying churners.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the concept that accuracy is misleading in imbalanced datasets, and candidates mistakenly choose precision or F1-score because they seem more comprehensive, but the question specifically asks for the metric that reveals the model's failure to identify churners, which is recall.
Detailed technical explanation
How to think about this question
Recall is defined as TP / (TP + FN), where FN represents churners incorrectly labeled as non-churn. In highly imbalanced datasets, accuracy is dominated by the majority class, so a naive classifier that always predicts the majority class achieves high accuracy but zero recall for the minority class. Real-world churn detection systems often use recall as a primary metric because the cost of missing a churner (e.g., lost revenue) far outweighs the cost of false alarms, and models are tuned to maximize recall while maintaining acceptable precision.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Recall (TPR) — Recall (True Positive Rate) measures the proportion of actual churners correctly identified by the model. With 80% non-churn and 20% churn, a model can achieve 95% accuracy by simply predicting the majority class (non-churn) for all samples, resulting in zero true positives for churn. Recall directly exposes this failure by quantifying how many churners are captured, making it the critical metric for imbalanced classification problems.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on AI0-001
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 training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with 99% legitimate and 1% fraudulent. Which evaluation metric should be prioritized to assess model performance?
easy- A.Accuracy
- ✓ B.F1-score
- C.Mean Squared Error
- D.Log Loss
Why B: In a highly imbalanced dataset (99% legitimate, 1% fraudulent), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy without detecting any fraud. The F1-score combines precision and recall into a single metric, making it the preferred choice for evaluating binary classification performance on imbalanced data, as it penalizes both false positives and false negatives equally.
Variation 2. A data scientist is building a classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraudulent cases. Which approach should the scientist use to evaluate model performance most effectively?
easy- ✓ A.F1 score
- B.Accuracy
- C.Recall
- D.Precision
Why A: In highly imbalanced datasets like fraud detection (1% positive class), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy yet fail to detect any fraud. The F1 score (harmonic mean of precision and recall) is the most effective metric because it balances both false positives and false negatives, providing a single score that reflects the model's ability to correctly identify the minority class without being skewed by class imbalance.
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Last reviewed: Jul 4, 2026
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