- In the navigation pane of Prediction Studio, click Models.
- Open the adaptive model that you want to edit, and then click the Settings tab.
- In the Model update frequency section, in the Update
model after every field, enter the number of responses that trigger the
update.When you update a model, ADM retrains the model with the specified number of responses. After the update, the model becomes available to the client nodes for scoring and the Pega Platform components that use the model.
Note: You should choose model update frequency so that for the average model instance, it would be updated every 2 to 4 hours. For example, if a model instance (such as a web banner for an offer) has 166 impressions per minute, then 20,000 (2*60*166=20,000) may be a good number for the adaptive model rule in that channel. The default setting is 5000. If the model update frequency is set too low, then the demand on the ADM nodes will be high. In addition to this, a model update will also be done at least every 12 hours, to make sure all recorded responses are regularly processed.
- In the Recording historical data section, specify if you want to
extract historical customer responses from adaptive models in your application.For more information, see Extracting historical responses from adaptive models.
- Modify advanced settings of a model by following the steps in one of the respective topics:
- 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.
- Click Save.
- Configuring advanced Bayesian model settings
Configure data analysis binning, predictor selection, and other advanced settings that control how a Bayesian adaptive model operates.
- Configuring advanced settings for an adaptive model based on gradient boosting
Configure advanced settings that control the operation and learning of an adaptive boosting model. Properly configured adaptive boosting models are more likely to return relevant results.
- Extracting historical responses from adaptive models
Extract historical customer responses from adaptive models in your application for offline analysis. You can also build a model in a machine learning service of your choice, based on the historical responses that you extract.
- JSON file structure for historical data
To perform better offline analysis of adaptive model historical data, learn more about the parameters that Pega Platform uses to describe the data that you extract.