Viewing the training results of an ML model element

You can view the results of training predictive elements and elliptic envelope-based elements.

System administrators and users who have the Train models permission from the Manage ML models group of rights can view the results of training ML model elements. The functionality is available after a license key is added.

To view the training results of an ML model element:

1. In the main menu, select the Models section.
2. In the asset tree, select the ML model element whose training result you want to view.

   A panel with the settings of the selected element will appear on the right.

3. Select the Training results tab.

If the ML model element has been successfully trained, the following information about the training results is displayed in the Training results tab:

• Message about successful completion of training of an ML model element.

  If you want to view the training settings for an element that were specified during its creation, click the Training settings button.

• User: The name of the user who started training the ML model element.
• Start of training: The date and time when the Trainer service began training the ML model element.
• End of training: The date and time that training of the ML model element finished. ML model element weights have been updated by the Trainer service.
• Training interval: The time spent by the Kaspersky MLAD server for training the ML model element.
• Total training duration: The duration of data time intervals considering the markups in the training dataset.
• Number of UTG nodes: The number of UTG nodes included in the training set.
• Graphs with learning results for ML model predictive elements:
  • Training and validation errors: A graph showing the training and validation errors for each training epoch.
  • Model prediction: Graphs showing model predictions for the output tags and the overall prediction error.
• Graphs with learning results for ML model elliptic envelopes:
  • Tag deviation—a graph showing the distance of a point, representing the state of a monitored asset at every moment in time within the phase space, from the center of the elliptical region of normal states. The orange horizontal line marks the threshold. It indicates the farthest point at which a condition can still be considered normal.
  • Tag values: graphs showing the values of each tag during training.
  • Tag value distribution: histograms that show the distribution of values for each tag during training.
  • Tag correlation: matrix that shows relationships between tags used when training an ML model element.

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