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Importing a predictive model

Updated on May 17, 2024

Import predictive models from third-party tools to predict customer actions. You can import PMML and H2O models.

You can import models from both H2O-3 and H2O Driverless AI platforms. For a list of supported PMML and H2O models, see Supported models for import.

Before you begin: Download the model that you want to import to your local directory:
  • For PMML models, download the model in PMML format.
  • For H2O-3 models, download the model in .mojo format.
  • For H2O Driverless AI models, download and extract the MOJO Scoring Pipeline file as a .zip file.

If you want to import a model from the H2O Driverless AI platform, specify the Driverless AI license key and import the H2O implementation library. For more information, see Specifying the H2O Driverless AI license key and Importing the H2O implementation library.

  1. In the navigation pane of Prediction Studio, click Models.
  2. In the header of the Models work area, click NewPredictive model.
  3. In the New predictive model dialog box, enter a Name for your model.
  4. In the Create model section, click Import model.
  5. Click Choose file and select a model file to import.
    For Driverless AI models, in the mojo-pipeline folder, select the pipeline.mojo file.
  6. In the Context section, specify the model context:
    Save your model in the default application contextSelect the Use default context check box.

    For more information, see Configuring the default rule context.

    Save your model in a custom context
    1. Click the Apply to class field, press the Down arrow key, and then select the class in which you want to save the model.
    2. Define the class context by selecting appropriate values from the Development branch, Add to ruleset, and Ruleset version lists.
  7. Verify the settings and click Next.
  8. Optional: To change the default label for the model objective, in the Outcome definition section, click Set labels, and then enter a meaningful name in the associated field.
    Note: To capture responses for the model, the model objective label that you specify should match the value of the .pyPrediction parameter in the response strategy (applies to all model types).
  9. In the Outcome definition section, specify what the model predicts:
    You are importing a binary outcome model
    1. In the Monitor the probability of field, select the outcome that you want to predict.
    2. In the Advanced section, enter the expected score range.
    3. In the Classification output field, select one of the model outputs to classify the model.
    You are importing a continuous outcome model
    1. In the Predicting list, select A continuous value.
    2. In the Predicting values between fields, enter values for the range of outcomes that you want to predict.
    You are importing a categorical outcome modelIn the Predicting section, verify the categories to predict.
  10. Optional: To compare actual model performance against expected model performance, in the Expected performance field, enter a value that represents the expected predictive performance of the model.
    The performance measurement metrics are different for each model type. For more information, see Metrics for measuring predictive performance.
  11. Confirm the model settings by clicking Import.
  12. On the Mapping tab, associate the model predictors with Pega Platform properties.
    For more information, see Editing an imported model.
  • Previous topic Template categories for creating predictive models
  • Next topic Specifying the H2O Driverless AI license key

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