Pegasystems PEGACPDS88V1 - Certified Pega Data Scientist 8.8

In Prediction Studio, model transparency thresholds can be set for a model.

In Prediction Studio, the key metrics of adaptive models are visualized in a bubble chart. The three key metrics displayed in this chart are number of responses, propensity of the model, and performance of the model.

When defining outcomes for an adaptive model, you must define one or more positive behavior values, which indicate that the customer accepted or responded to the offer. You can also define negative and neutral behavior values, but they are optional. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/configuring-adaptive-models

Customer name is usually not appropriate to be used as a predictor. A predictor is a property that influences the customer behavior and can be derived from various sources such as customer profile, interaction history, proposition details, etc. Customer name is not likely to have any impact on the customer’s preferences or responses, and it may also violate privacy regulations. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

The mapping of the input fields of a third-party predictive model is done in the Predictive Model rule. The Predictive Model rule defines how to invoke and interpret the results of a third-party predictive model that is imported in PMML format. References: https://academy.pega.com/module/predictive-analytics/topic/using-pmml-models

Given the scenario where U+ Bank introduces a new credit card that has no historical customer behavior data and wants to offer this credit card on the personalized web portal, you must use an adaptive model.

 A text categorization model is a type of text analytics model that can analyze the incoming messages and assign them to predefined categories, such as positive, negative, or neutral sentiment. This way, CSRs can focus on negative messages that require immediate attention or escalation. References: https://academy.pega.com/module/text-analytics/topic/creating-text-categorization-model

To detect the sentiment of the incoming emails, you need to configure a text analytics prediction1234 in Prediction Studio. A text analytics prediction is a type of prediction that uses natural language processing (NLP) to analyze text data and extract insights, such as topics, entities, and sentiments. You can use a text analytics prediction to detect the sentiment of an email based on its content and assign a score ranging from -1 (negative) to 1 (positive). This can help you improve the customer experience by identifying dissatisfied customers and taking appropriate actions.