Isolation Forest Outlier Detector
Anomaly or outlier detection has many applications, ranging from preventing credit card fraud to detecting computer network intrusions. The implemented Isolation Forest outlier detector aims to predict anomalies in tabular data. The anomaly detector predicts whether the input features represent normal behaviour or not, dependent on a threshold level set by the user.
The Isolation Forest is trained by running the
train.py script. The
OutlierIsolationForest class inherits from
CoreIsolationForest which loads a pre-trained model and can make predictions on new data.
A detailed explanation of the implementation and usage of Isolation Forests as outlier detectors can be found in the isolation forest doc.
Running on Seldon
An end-to-end example running an Isolation Forest outlier detector on GCP or Minikube using Seldon to identify computer network intrusions is available here.
Docker images to use the generic Isolation Forest outlier detector as a model or transformer can be found on Docker Hub:
A model docker image specific for the demo is also available: