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Isolation Forest Outputs

The following outputs can be generated by the Isolation Forest tool. Depending on your configuration and analysis requirements, you can select one or more outputs from the Outputs section.

Ouptut

Type

Description

Anomalies

Condition

Indicates whether an observation is considered anomalous based on the trained Isolation Forest model and anomaly detection settings.

Anomaly Score

Signal

Measures how unusual an observation is compared to the training data. Higher scores indicate a greater likelihood that the observation is anomalous.

Model input properties

Scalar

Provides information about the model inputs used during training. This output can be used for model validation and troubleshooting.

Training window

Condition

Identifies the time range used to train the Isolation Forest model. This output can be used to verify the training period and compare it with scored data.

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