Configuring Adaptive Model settings

Configure the update frequency and other settings that control how an adaptive model operates.
  1. In Dev Studio, click Records > Decision > Adaptive Model.
  2. Open an adaptive model that you want to edit and click the Settings tab.
  3. In the Model update frequency section, in the Update model after every field, enter the number of responses that trigger the update.
    When a model is updated, it is retrained with the specified number of responses and the new model is made available to the client nodes for scoring and the Pega Platform components that are using the model.
  4. In the Advanced Settings section, choose the update scope:
    • To use all received responses for each update cycle, select the Use all available responses option.
    • To assign more weight to recent responses when updating a model, select the Use subset of responses option
  5. In the Monitor performance for the last field, enter the number of weighted responses used to calculate the model performance that is used in monitoring.
    The default setting is 0, which means that all historical data is to be used in performance monitoring.
  6. In the Data analysis binning section, in the Grouping granularity field, enter a value between 0 and 1 that determines the granularity of the predictor binning.
    The higher the value, the more bins are created. The value represents a statistical threshold that indicates when predictor bins with similar behavior are merged. The default setting is 0.25.
    Note: This setting operates in conjunction with Grouping minimum cases to control how predictor grouping is established. The fact that a predictor has more groups typically increases the performance, however the model might become less robust.
  7. In the Grouping minimum cases field, enter a value between 0 and 1 that determines the minimum percentage of cases per interval.
    Higher values result in decreasing the number of groups, which can be used to increase the robustness of the model. Lower values result in increasing the number of groups, which can be used to increase the performance of the model. The default setting is 0.05.
  8. In the Predictor selection section, in the Activate predictors with a performance above field, enter a value between 0 and 1 that determines the threshold for excluding poorly performing predictors.
    The value is measured as the coefficient of concordance (CoC) of the predictor as compared to the outcome. A higher value results in fewer predictors in the final model. The minimum performance of CoC is 0.5, therefore the value of the performance threshold should always be set to at least 0.5. The default setting is 0.52.
  9. In the Group predictors with a correlation above field, enter a value between 0 and 1 that determines the threshold for excluding correlated predictors.
    The default setting is 0.8. Predictors that have a mutual correlation above this threshold are considered similar, and only the best of those predictors are used for adaptive learning. The measure is the correlation between the probabilities of positive behavior of pairs of predictors.
  10. In the Audit history section, to capture adaptive model details in the work object's history, select the Attach audit notes to work object check box.
    CAUTION:
    Enabling this setting causes significant performance overhead.