What component is used in Pega Decisioning to randomly select between two predictive models? (Choose One)

The Champion Challenger component is designed specifically for testing and comparing the effectiveness of different predictive models in a decisioning strategy. By using this component, organizations can randomly select between two or more models, allowing for a fair assessment of their performance based on real-time data.

In decisioning scenarios, the Champion Challenger approach enables businesses to continually optimize their models by evaluating which one performs better in terms of metrics such as conversion rates or profitability. This approach supports a cycle of iterative improvement and data-driven decision-making.

Other options might offer different functionalities within Pega Decisioning, but they do not serve the purpose of random selection between predictive models like the Champion Challenger component does. For instance, the Model component is focused on defining the characterization of a predictive model but does not inherently handle comparisons. The Random Select component may imply randomness but is not specifically designed for model evaluation in a decision-making context. Finally, the Switch component involves conditional logic to direct processing flow but does not pertain directly to model comparison or selection. Thus, the Champion Challenger component is uniquely suited for this scenario.