En

Anomaly Detection#

Anomaly detection based on a trained machine learning model.

Path to Model[Text] Path to the model file. The model must be created using the "Anomaly Detection Model Training" block.
Data[Data Table] Input data. The columns in the table must match the columns on which the model was trained.
Target Column Name

[Text] The name of the column for recording the result. If the column does not exist in the incoming data to the block, it will be added. The algorithm allows recording the label and score in different columns. To do this, specify two column names separated by a comma in this property.

For example, "PredictedLabel,Score".

Result[Data Table] The result of anomaly detection.
Error Handling Level

Select the error handling level. Possible values:

  • "Default" - default;
  • "Ignore" - errors are ignored;
  • "Handle" - errors are handled.

If "Default" is selected, the value from the "Start" block of this diagram will be used.

Message Level

Select the message level that blocks will output during operation. Possible values:

  • "Default" - default;
  • "Release" - output is disabled;
  • "Debug" - main information output;
  • "Detailed" - detailed information output.

If "Default" is selected, the value from the "Start" block of this diagram will be used.

Error Message[Text] Returns detailed information about the error in case of incorrect execution of the block's work.