AI0-001 Machine Learning and Deep Learning Practice Question
Which TWO are evaluation metrics for classification problems? (Choose two.)
⚠ Common exam trap
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.
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.
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.
- ✗
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.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 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 AI0-001 exam.