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Replacing a predictive model

3 Tasks

10 mins

Visible to: All users
Beginner Pega Customer Decision Hub 8.7 English
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U+ Bank uses Pega Customer Decision Hub™ to personalize the credit card offers that a customer receives on its website. The bank makes a proactive retention offer instead of a credit card offer for customers that are likely to leave the bank soon. The bank has recorded historical churn data for its customer base. As a data scientist, use the historical data to create a churn model using an external machine learning service, and then replace the active model with the new one.

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

Role User name Password
Data scientist DataScientist rules
Caution: This challenge requires specific artifacts. Ensure that you click Initialize (Launch) Pega instance for this challenge to retrieve the correct exercise system.

Your assignment consists of the following tasks:

Task 1: Create a validation data set

Use ValidationData to create a data set for model validation.

Task 2: Place the new model in shadow mode

Place the ChurnH20 model in shadow mode in the Predict churn propensity prediction.

Task 3: Promote the new model to the active state

As a data scientist, replace the active model with the candidate model in the prediction.


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 Create a validation data set

  1. On the exercise system landing page, click Pega CRM suite to log in to Customer Decision Hub.
  2. Log in as a data scientist with User name DataScientist and Password rules.
  3. In the navigation pane of Prediction Studio, click Data > Data sets to view the list of data sets.
  4. On the Data sets page, in the upper right, click New to add a new data set.
  5. Configure the new data set with the following information:
    1. In the Name field, enter ValidationData.
    2. In the Type list, select File.
    3. In the Apply to field, enter or select UBank-Data-Customer.
    4. In the Development branch list, keep the default No branch.
    5. In the Add to ruleset list, select CDH-Artifacts.
    6. In the Ruleset version list, select 01-01-01.
      new data set
  6. Click Create to build the data set.
  7. On the File tab of the data set, in the File location section, select Embedded file.
  8. Download the ValidationData data set to your local machine, and then extract the .csv file
  9. In the File Management section, click Upload file.
  10. In the Upload file dialog box, click Choose File.
  11. Select the ValidationData.csv file.
  12. In the Upload file dialog box, click Upload.
  13. In the upper right, click Save.

2 Place the new model in shadow mode

  1. Download the ChurnH20 model to your local machine.
  2. In the navigation pane of Prediction Studio, click Predictions to open the Predictions work area.
  3. Click the Predict Churn Propensity tile to open the prediction.
    Model tile
  4. On the Models tab, in the ChurnPML row, click the More icon, and then select Replace model.
    replace model
    1. In the Replace model dialog box, ensure that Model is selected, and then click Next.
    2. On the Upload tab, in the Select a PMML, H2O MOJO or Pega OXL file section, click Choose File.
    3. Select the file, and then click Next.
    4. In the Dataset list, select ValidationData.
    5. In the Outcome column list, select Outcome.
      Replace model
    6. Click Next.
    7. In the Model name field, enter ChurnH20.
    8. Click Replace.
      Note: During the process of replacing the model, the status changes to CONFIGURATION IN PROGRESS, VALIDATION IN PROGRESS, and then READY FOR REVIEW.
  1. On the Models tab, when the status of the ChurnH20 model changes to READY FOR REVIEW, click ChurnH20 (M-2001).
    click h2o
  2. On the Analysis tab, inspect the model comparison.
  3. In the upper right, click Evaluate.
  4. In the Evaluate ChurnH20 dialog box, confirm that Approve new candidate model and start shadowing (recommended) is selected.
  5. In the Reason field, enter The new model outperforms the active model, and then click Save to return to the prediction strategy.




3 Promote the new model to the active state

  1. On the Models tab, in the ChurnH20 row, click the More icon, and then select Promote model.
  2. In the Promote ChurnH20 dialog box, click Promote model to confirm that you want to promote the model.
  3. On the Models tab, confirm that the ChurnH20 model is now the active model.
    Active model
  4. In the upper right, click Save.

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