While predictive scoring allows you to make predictions about future outcomes, prescriptive scoring allows you to see how certain changes might affect future outcomes. After you have generated a prediction model (also called training a model), you can modify the prescriptive attributes in your data (those attributes marked as levers) to alter the predictions. The prescriptive scoring process evaluates each lever attribute, and returns an optimal value for that feature, depending on whether you want to minimize or maximize the goal variable.

Prescriptive scoring results include both an original score (the score before any lever attributes are changed) and an optimized score (the score after optimal values are applied to the lever attributes). In addition, for each attribute identified in your data as a lever, original and optimal values are included in the prescriptive scoring results.

ThingWorx Analytics prescriptive scoring can only be accessed via a ThingWorx API. In ThingWorx Composer, prescriptive scoring is accessible through the services of a Prescriptive Thing. These services can be used to run either synchronous real time prescriptive scoring or asynchronous batch prescriptive scoring. Requires installation of both ThingWorx Foundation and ThingWorx Analytics Server.