When setting up a decision tree in Pega, what is crucial for evaluating outcomes based on inputs?

The selection of well-defined inputs is essential for the effectiveness of a decision tree in Pega because the accuracy and clarity of the branching depend on the quality and specificity of these inputs. Each input acts as a criterion that drives the decision-making process within the tree structure. When inputs are clearly defined, it enables the system to properly categorize and evaluate the data, leading to precise outcomes for each decision path.

For example, if a decision tree is designed to assess customer eligibility for a service, the inputs such as age, income level, and previous client interactions must be clearly defined and understood. This clarity allows the decision tree to correctly evaluate these conditions and direct the flow of the case to the appropriate outcomes based on the pre-set rules.

In contrast, other options do not adequately support the primary function of a decision tree. A tree structure reflecting user roles would not necessarily enhance its ability to evaluate outcomes accurately—rather, the focus should be on the decisions being made. Linking decision nodes to separate case types might complicate the structure without improving decision clarity. Lastly, while numeric decisions can be straightforward, decision trees in Pega typically accommodate various data types, including qualitative measures, which further expand their applicability beyond just numeric outcomes. Thus, having well-defined inputs is