Which data transformation strategy would likely improve the performance of your classifier?

You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.

Which data transformation strategy would likely improve the performance of your classifier?
A . Write your data in TFRecords.
B . Z-normalize all the numeric features.
C . Oversample the fraudulent transaction 10 times.
D . Use one-hot encoding on all categorical features.

View Answer

Answer: C

Explanation:

Reference: https://towardsdatascience.com/how-to-build-a-machine-learning-model-to-identify-credit-card-fraud-in-5-stepsa-hands-on-modeling-5140b3bd19f1