Pegasystems PEGACPDS88V1 - Certified Pega Data Scientist 8.8

Pega Process AIâ„¢ 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.

 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: https://academy.pega.com/module/predictive-analytics/topic/mlops

When performing regular inspection of adaptive models, two issues that should be discussed with the business are predictors with a low performance and actions that are offered so often that they dominate other actions.

The purpose of predictions is to build adaptive models.

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 prioritize component is used to order actions based on a numerical value, such as priority, propensity, or custom expression. You can choose to sort the actions in descending or ascending order. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/prioritizing-actions

A predictive model is a mathematical function that estimates the probability of an outcome based on input data. When building a predictive model, the use of testing and validation samples increases the accuracy of models123. Testing and validation samples are subsets of data that are used to evaluate how well a model performs on new data that was not used to train the model. Testing and validation samples help prevent overfitting, which is when a model learns too much from the training data and fails to generalize to new data.

Performance and evidence are two of the results of an adaptive model. Performance is the percentage of positive responses that the model predicts for a given predictor profile. Evidence is the number of customers who exhibited statistically similar behavior. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

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