- A
Precision
Correct: Precision is a classification metric.
- B
Mean Absolute Error
Why wrong: MAE is for regression.
- C
R-squared
Why wrong: R-squared is for regression goodness-of-fit.
- D
Mean Squared Error
Why wrong: MSE is for regression.
- E
Recall
Correct: Recall is a classification metric.
Question 437 of 1,000
Classification Evaluation Metrics: Precision and Recall
This AI0-001 practice question tests your understanding of machine learning and deep learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which TWO are evaluation metrics for classification problems? (Choose two.)
Quick Answer
The answer is Precision and Recall. These two are correct because they directly measure a classification model’s ability to correctly identify positive instances, focusing on the relevance of predictions (Precision) and the completeness of capturing actual positives (Recall). On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish classification evaluation metrics from regression metrics, a common trap where learners confuse error-based measures like Mean Squared Error or R-squared with classification performance. The exam often presents a mix of metric types, so remember that Precision and Recall are specifically for binary or multi-class classification, not for continuous value prediction. A helpful memory tip: think of Precision as “out of what I predicted positive, how many were right?” and Recall as “out of all actual positives, how many did I catch?” — together, they form the foundation of classification evaluation.
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
✓
Precision
Precision is a classification metric that measures the proportion of true positive predictions among all positive predictions made by the model. It is calculated as TP / (TP + FP) and is critical when the cost of false positives is high, such as in spam detection or fraud alert systems.
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 this is correct
Correct: Precision is a classification metric.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Mean Absolute Error
Why it's wrong here
MAE is for regression.
- ✗
R-squared
Why it's wrong here
R-squared is for regression goodness-of-fit.
- ✗
Mean Squared Error
Why it's wrong here
MSE is for regression.
- ✓
Recall
Why this is correct
Correct: Recall is a classification metric.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between classification and regression metrics, and the trap here is that candidates may mistakenly select Mean Absolute Error or Mean Squared Error because they are common evaluation metrics, but they are exclusively used for regression problems, not classification.
Detailed technical explanation
How to think about this question
Precision and Recall are derived from the confusion matrix, where Precision focuses on the accuracy of positive predictions, while Recall (True Positive Rate) measures the model's ability to capture all actual positives. In imbalanced datasets, these metrics are often combined into the F1-score (harmonic mean of Precision and Recall) to provide a single balanced measure. Real-world scenarios like medical diagnosis prioritize Recall to avoid missing true cases, whereas legal document review prioritizes Precision to minimize false accusations.
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: Precision — Precision is a classification metric that measures the proportion of true positive predictions among all positive predictions made by the model. It is calculated as TP / (TP + FP) and is critical when the cost of false positives is high, such as in spam detection or fraud alert systems.
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.
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Last reviewed: Jul 4, 2026
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