What does a Scorecard model in PMML primarily do?

A Scorecard model in Predictive Model Markup Language (PMML) primarily evaluates and scores predictive behaviors. This model aims to assess the probability of an outcome based on various input variables, often used in credit scoring and risk assessment applications.

The scorecard generates scores by applying a set of predefined weights and thresholds to different input features, which help organizations make informed decisions based on the likelihood of certain behaviors, such as defaulting on a loan or responding positively to a marketing campaign. This scoring mechanism highlights the relative risk or opportunity associated with different scenarios, making it a vital component in decision-making processes.

The other options, while relevant to data analysis, do not describe the primary function of a Scorecard model. For example, regression analysis involves establishing relationships between variables rather than focusing solely on scoring. Segmenting customer data and generating marketing insights are also important tasks but typically fall under different types of modeling or analytical processes that are not specific to the scorecard approach.