AWS Certified AI PractitionerAI/ML and Generative AI FundamentalsMedium
A financial institution is implementing a machine learning model to detect fraudulent transactions. The model is designed to flag suspicious activities, and the primary concern is to minimize the number of actual fraudulent transactions that are missed. Which evaluation metric should the institution prioritize to ensure this objective is met?
- AAccuracy
- BPrecision
- CRecall
- DF1-Score
Show answer & explanationAnswer & explanation
Correct answer: C. Recall
The institution's primary concern is to minimize missed actual fraudulent transactions. This directly relates to 'false negatives' (actual fraud not detected). Recall (also known as Sensitivity) measures the proportion of actual positive cases that are correctly identified by the model. High recall means fewer missed fraudulent transactions.
Why the other options are wrong
- A. Accuracy measures the overall correctness of the model across all classifications, which can be misleading in imbalanced datasets.
- B. Precision measures the proportion of positive identifications that were actually correct, focusing on minimizing false positives.
- D. F1-Score is the harmonic mean of Precision and Recall, providing a balance between the two, but does not prioritize minimizing false negatives specifically.
Recall (Sensitivity)
A metric that measures the proportion of actual positive cases that are correctly identified by a model. It is calculated as True Positives / (True Positives + False Negatives).
- Focuses on minimizing False Negatives (missed actual positives).
- Important when the cost of missing a positive is high (e.g., fraud, disease detection).
- Also known as Sensitivity or True Positive Rate.
Memory trick: PR-F1, Accuracy's good, but Recall finds all fraud.