Machine Learning Scenario

This page is accessed via Logistics Machine Learning > Machine Learning Scenario.

A scenario defines one or more particular use cases on which you want to run machine learning algorithms. For example, consider shipments with a particular transport mode, such as truckload, LTL, and parcel.

You can include scenarios as part of a machine learning project by clicking New Machine Learning Scenario on the Machine Learning Project screen. This allows you to run training for all of the scenarios on the project and compare the results. For example, you can include three scenarios: one for truckload, one for LTL, and one for parcel transport modes. Then, you can compare the results.

Machine learning scenarios allow you to use the following to limit your historical data:

• Scenario filter allows you to select a different set of shipments to consider in each of your scenarios.
• Scenario Outlier filter allows you to select a different set of shipments or orders and identify outliers. For shipments, this is based based on transit time to be excluded from training.
• Excluded columns allows you to exclude attribute columns.

Adding a Machine Learning Scenario

1. Enter a meaningful Scenario ID.
2. Enter a Scenario Name such as ETA_NA.USA_TL.
3. Enter a Description to capture the purpose of the scenario.
4. Select a Domain Name.
5. Enter the Project ID if this is created directly. If creating the modeling scenario from a modeling project, the project ID is entered automatically.
6. Select a Model Type depending on what is on the machine learning project:

• If the machine learning project has a Model Type of Planned ETA Prediction, you see a single option of Planned ETA Prediction. This option creates machine learning models that only consider shipment-level histories and are good for predicting shipments that are either not executed or do not have any tracking events.
• If the machine learning project has a Model Type of Planned and Event based ETA Prediction, you see two options: Event Based ETA Prediction and Planned ETA Prediction.
  
  Event Based ETA Prediction creates machine learning models that not only considers shipment-level information but also tracking events. These models are good for predicting shipments that are in transit.
• If the machine learning project has a Model Type of Order Route Prediction, you see a single option of Order Route Prediction. This model type uses historical order data (trend/seasonality in lanes, volumes etc.) to show the most probable routes along with their corresponding probabilities.

7. Create filters.
   • Scenario Filter
   • Scenario Outlier Filter

8. Add Excluded Columns.

Scenario Filters

Use scenario filters to narrow down the list of shipments/orders based on shipment/order attributes. For example,

• only include shipments with the transport mode of TL
• only include shipments with a transport mode of TL, a source location country of USA, and a destination location country also of USA.

To create a new scenario filter:

1. In the Scenario Filters grid, click New Filter.
2. Enter a Filter Name
3. Select a Column Name.
4. Enter or select a Column Value and an operator. Not all operators are supported. The allowed operators are dependent on the Column Name.
5. Click Save.
6. Enter any additional shipment filter criteria as needed. Click Save after each.

If you define multiple scenario filter criteria, records satisfying all of the criteria would be included in the result set.

Scenario Outlier Filters

Outlier filters allow you to specify actual transit time constraints to limit shipments included in the scenario data. For example, you can add an outlier filter that excludes shipments where the transit time is less than 1 day and 12 hours or more than 10 days.

1. In the Outlier Filters grid, click New Filter.
2. Enter a Filter Name.
3. Optionally, enter Filter Criteria:
   1. Select a Column Name.
   2. Enter or select a Column Value and an operator. Not all operators are supported. The allowed operators are dependent on the Column Name.
   3. Click Save.
4. In the Outlier Criteria section, notice the Target Column with a hard-coded value of Actual Transit Time. You can only enter less than values and greater than values for the target of Actual Transit Time.
5. Enter one or both of the outlier bounds.
   • Enter a Less Than Value.
   • Enter a Greater Than Value.

Included and Excluded Columns

You can select certain columns and have them excluded from the scenario data. If excluded, a column cannot become a feature in the training model created for the scenario. Columns containing data required for training cannot be excluded and will not appear in the list.

Based on what is selected in the Modeling Type field on the project, this section changes as follows:

• If the Model Type is Planned ETA Prediction, you see a Planned ETA Prediction Model section. If you expand Shipment Columns you see all of the included columns and all flex field columns are excluded.
• If the Model Type is Planned and Event based ETA Prediction, you see both a Planned ETA Prediction Model section (as explained above) and an Event based ETA Prediction Model section. For Event based ETA Prediction Model you also see default included and excluded columns (which include some shipment columns and flex field columns).
• If the Model Type is 

Using the included and excluded column lists:

• To add columns to the Excluded Columns list, select one or more entries in the Included Columns list and click > or >> to move them to the Excluded Columns list.
• To remove columns from the Excluded Columns list, select one or more entries in the Excluded Columns list and click < or << to move them to the Included Columns list.

Deleting a Machine Learning Scenario

If you delete the project, all scenario data associated to the project is deleted. Also, the corresponding data in Logistics Machine Learning Intelligence is deleted.