Perform Training

This page is accessed via:

• Logistics Machine Learning > Machine Learning Project > Actions > Perform Training
• Logistics Machine Learning > Machine Learning Scenario > Actions > Perform Training

Note: The actions Load Data into Analytics and Export to Machine Learning Service must be completed before running this action.

Note: This action is only available for DBA.ADMIN and .ADMIN users.

Note: This action does not support the use of the Model Type of Order Route Prediction. This will be implemented in a future release.

The Perform Training action tells the machine learning service to start a training run based on the data from the scenario.

You can perform training from either a machine learning project or a machine learning scenario. If you perform training from a machine learning project, you see a page where you select multiple scenarios to run the training for. You can also run Perform Training as a process via process management.

Once you run the action, you see the Feature Importance Summary page:

• Project ID: This is the machine learning project ID associated to the scenario data used for training.
• Scenario ID: This is the ID of the machine learning scenario used for training.
• Training Status: Values are:
  • TRAINING NOT INITIATED
  • INITIATED
  • ACCEPTED: The machine learning service accepted the request to train machine learning scenario.
  • PROCESSING: The machine learning service is currently training the scenario.
  • COMPLETED: The training successfully completed.
  • FAILED: The training failed.
• Model Accuracy, Confidence Low Value, and Confidence High Value: Contain values sent to OTM from the machine learning service once the training completes.

Click Refresh to view the results of the training.

When the training is completed, the page updates to include a Feature Importance Summary grid which contains the following information:

• Project ID
• Scenario ID
• Feature: Significant characteristics of shipments that impact transit time when training a model.
• Percentage Contribution: Importance of the corresponding feature to the model.

If any of your scenarios fail to train, you see a Failure Reasons grid.