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anomalydetectionnode properties
anomalydetectionnode properties
Last updated: Feb 12, 2025
anomalydetectionnode properties

Anomaly node iconThe Anomaly node identifies unusual cases, or outliers, that don't conform to patterns of "normal" data. With this node, it's possible to identify outliers even if they don't fit any previously known patterns and even if you're not exactly sure what you're looking for.

Example

node = stream.create("anomalydetection", "My node")
node.setPropertyValue("anomaly_method", "PerRecords")
node.setPropertyValue("percent_records", 95)
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("peer_group_num_auto", True)
node.setPropertyValue("min_num_peer_groups", 3)
node.setPropertyValue("max_num_peer_groups", 10)
Table 1. anomalydetectionnode properties
anomalydetectionnode Properties Values Property description
inputs [field1 ... fieldN] Anomaly Detection models screen records based on the specified input fields. They don't use a target field. Weight and frequency fields are also not used. See Common modeling node properties for more information.
mode Expert Simple  
anomaly_method IndexLevel PerRecords NumRecords Specifies the method used to determine the cutoff value for flagging records as anomalous.
index_level number Specifies the minimum cutoff value for flagging anomalies.
percent_records number Sets the threshold for flagging records based on the percentage of records in the training data.
num_records number Sets the threshold for flagging records based on the number of records in the training data.
num_fields integer The number of fields to report for each anomalous record.
impute_missing_values flag  
adjustment_coeff number Value used to balance the relative weight given to continuous and categorical fields in calculating the distance.
peer_group_num_auto flag Automatically calculates the number of peer groups.
min_num_peer_groups integer Specifies the minimum number of peer groups used when peer_group_num_auto is set to True.
max_num_per_groups integer Specifies the maximum number of peer groups.
num_peer_groups integer Specifies the number of peer groups used when peer_group_num_auto is set to False.
noise_level number Determines how outliers are treated during clustering. Specify a value between 0 and 0.5.
noise_ratio number Specifies the portion of memory allocated for the component that should be used for noise buffering. Specify a value between 0 and 0.5.