Explanation
When building a predictive model, a valid predictor data type is Boolean, which can have only two values:
true or false. Other valid predictor data types are numeric, date, and symbolic (categorical). References:
https://academy.pega.com/module/predictive-analytics/topic/predictor-data-types

Explanation
To offer a 10% discount for customers whose CLV value is higher than 400, you should use the Filter strategy component.

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
This is because a customer's propensity to accept an offer depends on their past behavior and preferences. If a customer has accepted similar offers in the past, they are more likely to accept a new offer that matches their interests
https://academy.pega.com/sites/default/files/media/documents/2020-12/Mission20301-2-EN-StudentGuide.pdf

Explanation
Evidence is the number of customers who exhibited statistically similar behavior. It indicates how much data the model has collected for a given predictor profile. The higher the evidence, the more reliable the model is.
References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision