Question

A machine learning model is being used to detect fraudulent transactions in a banking system. Explain why recall might be a more important metric than precision in this context. Justify your answer with a real-world example of the consequences of false negatives versus false positives.

Name: a machine learning model is being used to detect fraudulent transactions in a banking system explain why recall might be a more important metric than precision in this context justify your a 87631
Uploaded: 2023-07-10T17:19:26-08:00
Duration: 2 min 29 s
Channel: Madhur L
Description: a machine learning model is being used to detect fraudulent transactions in a banking system explain why recall might be a more important metric than precision in this context justify your a 87631

          A machine learning model is being used to detect fraudulent transactions in a banking system. Explain why recall might be a more important metric than precision in this context. Justify your answer with a real-world example of the consequences of false negatives versus false positives.

Added by Shelby M.

Computer Science and Information Technology

Trishna Knowledge Systems 2018 Edition

Instant Answer

Solved by Expert Madhur L

07/10/2023

Step 1

Precision measures the proportion of predicted positive cases that were actually positive. In the context of fraud detection, recall focuses on identifying as many fraudulent transactions as possible, while precision focuses on the accuracy of those identified Show more…

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A machine learning model is being used to detect fraudulent transactions in a banking system. Explain why recall might be a more important metric than precision in this context. Justify your answer with a real-world example of the consequences of false negatives versus false positives.