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Training adaptive models

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Use the Call instruction with the DSMPublicAPI-ADM.pxUploadResponsesFromReport or Rule-Decision-AdaptiveModel.pyUploadResponsesFromReport activities to train adaptive models with data from a report definition on Interaction History fact records. The use of previous results allows for Adaptive Decision Manager to create models that can predict behavior.

  1. Create an instance of the Activity rule by clicking Records Explorer Technical Activity .

  2. In the activity steps, enter one of the following methods:

    • Recommended: Call DSMPublicAPI-ADM.pxUploadResponsesFromReport
    • Call Rule-Decision-AdaptiveModel.pyUploadResponsesFromReport
  3. Click the arrow to the left of the Method field to expand the method and specify its parameters:

    • Name of the report definition
    • Class of the report definition
    • Page of class Embed-Decision-OutcomeColumnInfo This page needs to provide pyName as the outcome column in the report definition that defines the behavior and map these values to the possible outcomes from which the adaptive model rule learns.
    • Page of class Embed-Decision-AdaptiveModel-Key - This page needs to provide the adaptive model parameters. Adaptive model parameters are values that point to the model in the channel ( pyChannel and pyDirection ), action dimension ( pyIssue, pyGroup, and pyName ), and class context ( pyConfigurationAppliesTo and pyConfigurationName ).
  4. Click Save.

    The report definition rule gathers the sample data. Only properties that are optimized for reporting when they have been created should be used in the report definition. The following example corresponds to a report definition that gathers work data. If the data is in an external data source, use the Connector and Metadata wizard to create the required classes and rules.

    Column source Column name Sort type Sort order
    .Outcome Outcome Highest to Lowest 3
    .Age Age Highest to Lowest 2
    .Credit History Credit History Highest to Lowest 1

  • Calling another activity
  • Keystores

    A keystore is a file that contains keys and certificates that you use for encryption, authentication, and serving content over HTTPS. In Pega Platform, you create a keystore data instance that points to a keystore file.

  • Types of Data Set rules

    Learn about the types of data set rules that you can create in Pega Platform.

  • Obtaining predictor information

    Use the Call instruction with the DSMPublicAPI-ADM.pxLoadPredictorInfo activity to obtain the predictor information of an adaptive model. Predictors contain information about the cases whose values might potentially show some association with the behavior that you are trying to predict.

  • About the Connector and Metadata wizard
  • Decision Management methods

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