Explanation
Predictive analytics is a branch of data science that uses statistical techniques and machine learning algorithms to find repeatable patterns in data and make predictions about future outcomes or behaviors. References:
https://academy.pega.com/module/predictive-analytics/topic/predictive-analytics-overview

Explanation
Pega Customer Decision Hub uses the PCV*L arbitration formula to select the next best action for each customer. The purpose of the formula is to balance customer needs with business objectives.

Explanation
Two tasks that a system architect needs to perform to export historical data are export the data set and create a data set.

Explanation
When selecting the list of predictors for an adaptive model you should consider properties from a wide range of sources. Predictors are properties that influence the customer behavior and can be derived from various sources such as customer profile, interaction history, proposition details, etc. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision

Explanation
Adaptive model components can output the customer's propensity to accept an action. Propensity is the likelihood of a positive response for a given action and predictor profile. It ranges from 0 to 100. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision