A company is interested in building a fraud detection model. Currently, the Data Scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST…

The correct answer is C. Oversampling using SMOTE. With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for…

Modeling

Question

A company is interested in building a fraud detection model. Currently, the Data Scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST likely to detect the GREATEST number of valid fraud cases?

Options

AOversampling using bootstrapping
BUndersampling
COversampling using SMOTE
DClass weight adjustment

How the community answered

(47 responses)

A
26% (12)
B
4% (2)
C
57% (27)
D
13% (6)

Explanation

With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting informatio

Topics

#Imbalanced Datasets#Oversampling#SMOTE#Data Preprocessing

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