We have several existing ADM models in production that we'd like to enhance with Adaptive Gradient Boosting, but we want to minimize disruption and avoid manual reconfiguration work if possible.

I have a few questions regarding the implementation of Adaptive Gradient Boosting in Pega ADM models:

1. Model Update Process: Is it possible to update an existing ADM model to use Adaptive Gradient Boosting as the predictor selection method without having to recreate the entire model from scratch? If yes, what is the recommended approach/process to do this?

2. Predictor Migration: When updating an existing model to use Adaptive Gradient Boosting, is there an automated way to migrate/transfer the existing predictors to the new configuration, or do I need to manually add them again through the prediction studio?

3. Impact on Model History: If such an update is possible, what happens to the existing model's performance history and training data? Will the model retain its historical data, or will it start fresh with the new configuration?