Suppose you have trained an anomaly detection system for fraud detection, and your system that flags anomalies when p(x) is less than ε, and you find on the cross-validation set that it is missing many fradulent transactions (i.e., failing to flag them as anomalies) . What should you do?

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Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]

Suppose you have trained an anomaly detection system for fraud detection, and your system that flags anomalies when p(x) is less than , and you find on the cross-validation set that it is missing many fradulent transactions (i.e., failing to flag them as anomalies) . What should you do?

A	Increase
B	Decrease
C	both a and b
D	none of these

Explanation:

Detailed explanation-1: -There are three main classes of anomaly detection techniques: unsupervised, semi-supervised, and supervised.

Detailed explanation-2: -Perhaps the most significant benefit of anomaly detection is the automation of KPI analysis. For most businesses, KPI analysis is still a manual task of sorting through all of their digital channel’s data across different dashboards.

There is 1 question to complete.

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