a photograph of a forest with felled logs covered with snow to symbolise the Random Cut Forest Algorithm

Random Cut Forest Algorithm

The Random Cut Forest Algorithm (RCF) is an unsupervised algorithm which is used to identify anomalies in data. An anomaly is a data point that differs significantly from the bulk of the data. The Random Cut Forest Algorithm provides a score for each data point. A low score indicates the datapoint is similar to the bulk of the data. A high score indicates an anomaly.

Attributes

Problem attributeDescription
Data types and formatTabular
Learning paradigm or domainUnsupervised Learning
Problem typeAnomaly detection
Use case examplesDetect abnormal behavior in an application

Training

Data for training the Random Cut Forest Algorithm can be in either CSV or x-recordio-protobuf format.

Model artifacts and inference

DescriptionArtifacts
Learning paradigmUnsupervised Learning
Request formatCSV, JSON, x-recordio-protobuf
ResultJSON, x-recordio-protobuf

Processing environment

CPU instances are used for both Training and Inferencing.

AWS Partner Webinar: Random Cut Forest on Amazon SageMaker

This is a 45.04 minutes video by Chris Burns from AWS.

Detect Anomalies in Your Data with Amazon SageMaker (Level 300)

This is a 29.13 minutes video by Will Badr from AWS.

Credits

Cut down trees photo by Olia Gozha on Unsplash

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