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MLA-C01 Practice Question: A data scientist has trained a binary…
A data scientist has trained a binary classification model for fraud detection. The dataset is highly imbalanced (99% non-fraud, 1% fraud). After evaluation, the model shows an accuracy of 99%, but the recall for fraud cases is only 10%. Which metric should the data scientist prioritize to improve the model's performance for fraud detection?
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
F1-score balances precision and recall, making it more informative than accuracy for imbalanced datasets. AUC-ROC is also used but F1 directly addresses the trade-off between false positives and false negatives. Precision alone does not capture recall, and Log loss does not directly indicate recall improvement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Log loss
Why it's wrong here
Log loss measures probabilistic predictions but does not specifically address recall.
- ✓
F1-score
Why this is correct
F1-score is the harmonic mean of precision and recall, making it a balanced metric for imbalanced classification.
- ✗
Precision
Why it's wrong here
Precision alone ignores recall, which is crucial for catching fraud cases.
- ✗
Area under the ROC curve (AUC-ROC)
Why it's wrong here
AUC-ROC is useful but does not directly reflect the precision-recall trade-off as clearly as F1 for highly imbalanced data.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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