Training an Anomaly Detection Model#

Creates a new machine learning model for anomaly detection, trains it based on input data, and saves the final model to a file.

Training Data	[Text] Path to the CSV file containing the training data. The file must contain valid headers. The file must be in UTF8 format.
Number of Components	[Number] Number of components in PCA (rank). Set to zero for automatic determination.
Data Column Numbers	[Text] Column numbers containing the data. Comma-separated. Indexing starts from zero. For example, `"1,3"`.
String Column Numbers	[Text] Column numbers containing text data. Comma-separated. If this value is left blank, the column type will be recognized automatically. Indexing starts from zero. For example, `"1,3"`.
Delimiter	[Text] CSV column delimiter.
Algorithm Type	Select the type of algorithm.
Model Path	[Text] Path to save the model file.
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.