Pegasystems PEGACPDS88V1 Dumps

 When building a predictive model, the Data Analysis stage is where you select the input data that will be used to train and test the model. You can also filter, group, or transform the input data to improve its quality and relevance. References:

As a data scientist, you create a prediction that calculates the probability that a client is likely to cancel a subscription. The type of prediction you create is Customer Decision Hub.

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

The calendar component is used to remove propositions based on the current month, day of week, or time of day. It can also be used to apply seasonal adjustments to propositions. References:

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:

To associate an adaptive model with each action in a decision strategy created from scratch, you must define the adaptive model instances in the Adaptive Decision Manager.

The filter component is used to filter actions based on various criteria, such as eligibility, suitability, priority, or custom conditions. References:

When an adaptive model has not collected any evidence, its performance is 0.5, which means that it has no predictive power and is equivalent to a random guess. As more evidence is collected, the performance can increase or decrease depending on how well the model predicts customer behavior. References:

 When you place the new model in shadow mode in the production environment, the current model still drives the prediction, but the new model runs in parallel and collects performance data for comparison. References:

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:

Predictions combine predictive analytics and best practices in data science. Two best practices included in Pega Customer Decision Hub™ predictions are using a control group and selecting the model with the highest performance.

When you increase the performance threshold setting of an adaptive model rule, the number of active predictors may decrease. The performance threshold is the minimum performance that a predictor must have to be included in the model. If you increase this value, some predictors may not meet the criteria and be excluded from the model. References:

Pega Process AI™ lets you bring your own predictive models to Pega and use predictions in case types to optimize the way your application processes work and meet your business goals.

To use the outcome of a predictive fraud model in the case type that processes the incoming claim, you need to use the model outcome in the condition of a decision step2. This way, you can route suspicious claims to a fraud expert for closer inspection based on the model’s prediction.

The key component of a Next-Best-Action strategy is a strategy, which is a graphical representation of the business logic that determines which actions to offer to each customer and in what order. A strategy can use various components, such as business rules, predictive models, filters, prioritizers, etc., to achieve this goal. References:

To identify emails that voice a complaint, the text prediction uses an entity extraction model.

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

The performance of an adaptive model that has not collected any evidence yet is 50. This means that the model is not confident about its predictions and assigns equal probability to all actions. References:

When implementing a Next-Best-Action project, the recommended first step is to define Issue and Group hierarchy, which are used to organize and categorize propositions based on business objectives and customer needs. This step helps to align the project with the business vision and goals. References:

In Customer Decision Hub™, the factor in the arbitration formula that is calculated by using AI is Propensity.

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:

You are a company with a new and unique product, and you want to offer it to the right customer. Given the scenario, you should use an adaptive model rule type. An adaptive model rule type allows you to define the predictors and the outcome of the model and associate it with an action. An adaptive model learns from customer responses in real time and predicts the propensity of each customer to accept the action. An adaptive model is suitable for new products or markets where there is no historical data available. References: