AIF-C01 Fundamentals of AI and ML Practice Question

A data scientist is building a binary classification model for fraud detection. The dataset is highly imbalanced (99% legitimate, 1% fraud). Which metric is most appropriate to evaluate model performance?

⚠ Common exam trap

The AIF-C01 exam often tests the misconception that accuracy is always the best metric, especially when candidates overlook the impact of class imbalance on model 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

✓

F1-score

In highly imbalanced datasets (99% legitimate, 1% fraud), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy but fail to detect any fraud. The F1-score is the harmonic mean of precision and recall, providing a balanced measure that accounts for both false positives and false negatives, making it the most appropriate metric for evaluating fraud detection models.

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 reliable for imbalanced datasets as it can be high even if the model predicts all samples as the majority class.
✓
F1-score
Why this is correct
F1-score is the harmonic mean of precision and recall, providing a balanced evaluation for imbalanced datasets.
✗
Recall
Why it's wrong here
Recall measures false negatives but ignores false positives, not ideal for imbalanced data.
✗
Precision
Why it's wrong here
Precision measures false positives but ignores false negatives, not ideal for imbalanced data.

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AIF-C01 Fundamentals of AI and ML Practice Question

Why each option matters

Option-by-option breakdown

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