An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website.The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.The historical transactions data is in a .csv file that is stored in Amazon S3.The data contains features such as the user’s IP address, navigation time, average time on each page, and the number of clicks for each session.There is no label in the data to indicate if a transaction is anomalous.
Which models should the company use in combination to detect anomalous transactions?
(Choose two.)
IP Insights
K-nearest neighbors (k-NN)
Linear learner with a logistic function
Random Cut Forest (RCF)
XGBoost
I outline that the answer is:
IP Insights