In Pega Decision Management, what is typically required to change the output of a predictive model?

In Pega Decision Management, adjusting the parameters based on feedback is essential for refining the output of a predictive model. This process often involves monitoring the model's performance over time and making data-informed adjustments to the parameters that control how the model interprets input data and generates predictions.

By incorporating feedback, analytics teams can continuously optimize the model to enhance its accuracy and relevance, ensuring it remains effective even as external conditions and data patterns change. This iterative approach allows for incremental improvements, helping to adapt the model to new trends or shifts in the underlying data without requiring a complete overhaul.

The other approaches, while potentially beneficial in specific scenarios, do not capture the systematic and responsive nature of model refinement that feedback adjustments provide. For instance, merely adjusting the input data alone may not address the underlying model's performance or its adaptability, while modifying the algorithm or recreating the model from scratch involves more significant changes that may not be necessary for simple performance improvements.