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.
|Data types and format||Tabular|
|Learning paradigm or domain||Unsupervised Learning|
|Problem type||Anomaly detection|
|Use case examples||Detect abnormal behavior in an application|
Data for training the Random Cut Forest Algorithm can be in either CSV or x-recordio-protobuf format.
Model artifacts and inference
|Learning paradigm||Unsupervised Learning|
|Request format||CSV, JSON, x-recordio-protobuf|
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.