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Add/retrain models

There are three methods for adding new models to your experiment:

This page describes adding and retraining for random- or stratified-partitioned experiments. To add or retrain date/time- partitioned experiments, see the time-aware modeling sections.

Can I create blender models?

DataRobot does not create blender models as part of Autopilot. To add a blender to your use case:

  1. Create an experiment and build models.
  2. Open the project in DataRobot Classic and blend models.
  3. Reopen the experiment in Workbench.

The blender will be present in the Leaderboard.

Rerun modeling

Use the Rerun icon to rerun Autopilot with a different feature list, a different modeling mode, or additional automation settings (for example, GPU support) applied. If you select a feature list that has already been run, Workbench will replace any deleted models or make no changes.

Train on new settings

Once the Leaderboard is populated, you can retrain any existing model, which will create a new Leaderboard model. To retrain, select a model from the Leaderboard by clicking it.

Change a model characteristic by clicking the change icon () next to the component in Training settings:

Change feature list (post-modeling)

To change the feature list:

Click the icon next to the current feature list and select a new feature list in the resulting modal. The current list is greyed out and unavailable for selection. Note that you cannot change the feature list for the model prepared for deployment because it is a "frozen" run.

Change sample size

To change the sample size:

Click the icon next to the reported sample size and enter a new value in the resulting modal. Note that when setting a new sample size, above a certain point (which is determined by the size of the dataset), DataRobot forces a frozen run. To increase sample size in larger datasets without a frozen run, create the new model from the blueprint repository. You can also choose to manually enforce a frozen run.

When set, click Train new models.

Change monotonic feature lists

To change the feature lists applied for monotonic modeling:

Click the icon next to Monotonic constraints and select at least one new feature list in the resulting modal. You can create monotonic feature lists prior to modeling or post-modeling to apply monotonic constraints. Note that if the model does not support monotonic constraints the label and icon are not displayed.

Blueprint repository

The blueprint repository is a library of modeling blueprints available for a selected experiment. Blueprints illustrate the tasks used to build a model, not the model itself. Model blueprints listed in the repository have not necessarily been built yet, but could be as they are of a type that is compatible with the experiment's data and settings.

There are two ways to access the blueprint repository:

Add models

Once in the repository, you can add one or more blueprints to your experiment. Note the badges under the blueprint name, which in some cases indicate support for special modeling flows. For example, the MONO badge identifies blueprints that support monotonic constraints.

  1. Click on a blueprint name to see the graphical representation of tasks that comprise it. Select blueprints by checking the box to the left of the blueprint name.

  2. Set the feature list and sample size to apply to all selected blueprints.

  3. Once the configuration is finished, click Train models to start building.

Search models

There are three ways to filter the repository display to show only those blueprints matching the selected criteria.

  • Use the search bar to return all blueprints with matching strings in the name or description:

  • Click a badge to return all blueprints with that badge:

    Click again on the badge to remove it as a filter.

  • Use Edit filters to choose blueprints by model family and/or property. Available fields, and the settings for that field, are dependent on the project and/or model type.

Next steps

From here, you can:

Updated June 20, 2024