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Importing predictive models

1 Task

10 mins

Visible to: All users
Beginner Pega Customer Decision Hub 8.8 English
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U+ Bank has recently implemented Pega Decision Management, but already uses predictive models that an external bureau created. You are asked to make an existing H2O model actionable in Pega Customer Decision Hub™. The model is based on the historical customer interactions with the bank and predicts the likelihood that a customer might churn in the near future.

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

Role User name Password
Data Scientist DataScientist rules

Your assignment consists of the following tasks:

Task 1: Import an H2O model

In Prediction Studio, create a predictive model rule by using the 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.


You must initiate your own Pega instance to complete this Challenge.

Initialization may take up to 5 minutes so please be patient.

Challenge Walkthrough

Detailed Tasks

1 Import an H2O model

  1. Download the model file to your local machine.
  2. On the exercise system landing page, click Pega CRM suite to log in to Customer Decision Hub.
  3. Log in as a data scientist:
    1. In the User name field, enter DataScientist
    2. In the Password field, enter rules.
  4. In the navigation pane of Customer Decision Hub, click Intelligence > Prediction Studio to open Prediction Studio.
  5. In the navigation pane of Prediction Studio, click Models to open the models landing page.
  6. In the upper-right corner, click New > Predictive Model to open the New predictive model dialog box.
  7. In the New predictive model dialog box, enter the following information:
    1. Name: ChurnH2O.
    2. Click Import model to select the model file.
    3. Click Choose File, and then, on your local machine, select the model file.
    4. In the Context section, enter UBank-, press the Down arrow key, and then select the UBank-CDH-Data-Customer from the list.
    5. In the Add to ruleset field, select CDH-Rules from the list.
      This image shows the new predictive model settings
  8. Click Next to proceed to the Outcome definition section.
  9. In the Outcome definition section, ensure that Predict the probability of is set to Churn. Do not alter the advanced settings.
  10. In the Expected performance (AUC) field, enter 80.
    This image shows the Outcome definition section
  11. Click Import to import the model file.
  12. On the Mapping tab, verify that all predictors of the model are correctly mapped to the fields of the data model.
    This image shows the Mapping tab
  13. Click Save to save the model.

Confirm your work

  1. In the upper-right corner, click Run to open the Run predictive model dialog box.
  2. In the Run predictive model dialog box, in the Inputs section, select data transform Troy as the data source.
    Troy data transform
  3. Click Run and then, in the Outputs section, verify that the segment for Troy is Churn.
    Troy result
  4. Re-run the model with data transform Barbara as the data source.
  5. In the Outputs section, verify that the segment for Barbara is Loyal.
    Barbara result

This Challenge is to practice what you learned in the following Modules:

Available in the following mission:

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