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As a data scientist, you are asked to create a prediction to optimize the click-through rate of a web banner. What type of prediction do you need to create in Prediction studio?
Customer Decision Hub Reference:
To optimize the click-through rate of a web banner, you need to create aCustomer Decision Hubprediction.
You can use various data types in adaptive analytics. Some of these require preprocessing before being used as a potential predictor. Others can be used directly. Which two data types require no preprocessing? (Choose Two)
Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and income Reference:
Dates with absolute time/date values, such as birthdaysandNumeric data such as customer age and incomerequire no preprocessing before being used as potential predictors in adaptive analytics.
As a data scientist, you are tasked with creating a new prediction that estimates a customers' likelihood to leave the business in the near future. The NBA analyst wants to move forward and use the prediction in Pega Customer Decision Hub to test the application. To unblock the NBA specialist, which task do you prioritize?
To unblock the NBA specialist, as a data scientist, you should prioritizecreating the predictive model that drives the prediction.
Lift is key metric for the performance of the adaptive models.
To measure lift, you need a____________
Lift is a key metric for the performance of the adaptive models. To measure lift, you need a control group with random actions. Lift is the ratio of the performance of the adaptive model to the performance of the control group. A control group is a subset of customers who receive random actions instead of the ones suggested by the model. Reference: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#data-/data-adm-/data-adm-model/main.htm
The adaptive model component in a decision strategy computes
The adaptive model component in a decision strategy computes a propensity value for each action. Propensity is the likelihood of a positive response for a given action and predictor profile. It ranges from 0 to 100. Reference: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm
