AI0-001 Machine Learning and Deep Learning Practice Question
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?
⚠ Common exam trap
It's easy for candidates to default to accuracy as the primary metric, not realizing that in highly imbalanced scenarios, accuracy can be artificially high and meaningless, while the F1-score reveals the true performance on 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
✓
F1-score
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Accuracy
Why it's wrong here
Accuracy is not suitable for imbalanced data as it can be high even if the model fails to detect fraud.
- ✓
F1-score
Why this is correct
F1-score balances precision and recall, making it ideal for imbalanced classification.
- ✗
Mean Squared Error
Why it's wrong here
Mean Squared Error is a regression metric and not applicable to classification.
- ✗
Log Loss
Why it's wrong here
Log Loss can be used but is less intuitive and does not directly address class imbalance like F1-score.
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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.