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The use of an imported third-party model in a decision strategy is similar to the use of a model built with Pega machine learning. You can use a predictive model component in a decision strategy to reference an imported third-party model and pass the input parameters and receive the output score. You do not need to convert the third-party model into a Pega machine learning model or Pega markup language. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-predictivemodel/main.htm

When developing a predictive model, the outcome value of a continuous model type can represent the purchase value of an offer.

In a decision strategy, the Adaptive Model decision component belongs to the Decision Analytics category. This category contains components that use advanced analytics techniques, such as adaptive models, predictive models, text analytics models, etc., to make predictions or recommendations. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/decision-analytics-components

The principle of parsimony states that from two models with similar performance, we choose the one with the fewest number of predictors. This is because a simpler model is easier to understand, maintain, and deploy. References: https://academy.pega.com/module/predictive-analytics/topic/evaluating-predictive-models

According to the Data Scientist Student Guide1, page 18, the correct answer is B. A predictive model and C. A control group. A predictive model is a mathematical representation of a real-world process that can be used to predict an outcome based on input data. A control group is a subset of customers who are not exposed to a treatment (such as an offer) and are used to measure the effectiveness of the treatment by comparing their behavior with the treated group.

The decision component that enables you to use a PMML model is Predictive Model. Predictive Model is a component that references a predictive model rule that defines the input parameters and the output score of the model. You can use a predictive model component to reference a PMML model that is imported from a third-party tool and use it in your decision strategy. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-predictivemodel/main.htm

Adaptive models perform a binary model calculation. This means that adaptive models predict the likelihood of a positive or negative response for each action and customer profile. Adaptive models do not require underlying predictive models or historical data sets to start learning. They learn from customer responses in real time and continuously update their predictions. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

Using Prediction Studio to build Pega machine learning models on historical data, you can build two types of models: binary models and adaptive models.

The Predictive Model Markup Language (PMML) allows for predictive models to be easily shared between applications. PMML is a standard XML format that describes the input parameters, output score, and mathematical formulas of predictive models. PMML enables interoperability between different tools and platforms that support PMML, such as Pega Customer Decision Hub. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#data-/data-predictivemodel-/data-predictivemodel-pmml/main.htm

Pega AI is primarily based on self-learning models Reference:

Pega AI is primarily based on self-learning models.