Skip to main content

Importing predictive models


2 Tasks

5 mins

Visible to: All users
Beginner Pega Customer Decision Hub 8.5 English
This content is now archived and is no longer updated. Progress is not calculated. Pega Cloud instances are disabled, and badges are no longer awarded. Click here to continue your progress in the latest version.


U+ Bank has recently implemented Pega Decision Management but has already been using predictive models created by an external bureau for some time. You have been asked to make an existing H2O model actionable in Customer Decision Hub™. The model is based on the bank’s historical customer interactions and predicts the likelihood that a customer will churn in the near future.

Use the following credentials to log in to the exercise system:


User name


Data Scientist



Your assignment consists of the following tasks:

Task 1: Import an H2O model

In Prediction Studio, create a predictive model rule, using the ChurnH2O.mojo model file.

Task 2: Test the model

Run the model using the Troy data transform. Troy has a high churn risk. Re-run the model using the Barbara data transform. Barbara has a low churn risk.

Challenge Walkthrough

Detailed Tasks

1 Import an H2O model

  1. Download and extract the ChurnH2O.mojo model file.
  2. Log in as a Data Scientist with user name DataScientist and password rules.
  3. In the navigation pane on the left, click Intelligence > Prediction Studio > Models.
  4. In the top right, click New > Predictive Model.
  5. In the New predictive model dialog box, enter the following information:
    1. Name: ChurnH2O.
    2. Click Import model.
    3. Click Choose File, and then select the ChurnH2O.mojo model file.
      New model
  6. Click Next.
  7. In the Outcome definition section, ensure that Predict the probability of is set to churned. Do not alter the advanced settings.
  8. Enter the Expected performance: 80.
    Outcome definition
  9. Click Import.
  10. On the Mapping tab, verify that all predictors of the model are correctly mapped to the fields of the data model.
    Mapping tab
  11. Click Save to save the model.

2 Test the model

  1. In the top right, click Run.
  2. In the Run predictive model dialog box, in the Inputs section, select data transform Troy as the data source.
    Inputs Troy
  3. Click Run and scroll down to the Outputs section. Verify that the segment for Troy is churned.
    Outputs Troy
  4. Re-run the model with data transform Barbara as the data source.
  5. Verify that the segment for Barbara is loyal.
    Outputs Barbara
  6. On the Batch run tab, select CustomerBatch as the data source.
  7. Click Re-run.
  8. For the output, select Results.
    Outputs batch
  9. Notice that model predicts that roughly 20% of the 10K customers in the data set are likely to churn.

Available in the following mission:

We'd prefer it if you saw us at our best.

Pega Academy has detected you are using a browser which may prevent you from experiencing the site as intended. To improve your experience, please update your browser.

Close Deprecation Notice