Which type of modeling does PMML support?

PMML, which stands for Predictive Model Markup Language, is designed to facilitate the sharing and deployment of predictive models across different systems. Its capabilities extend beyond mere representation of predictive models, enabling it to support a range of modeling types.

The correct choice highlights that PMML can handle both predictive models, such as regression and decision trees, as well as descriptive models, which provide insights into data relationships and patterns, and it supports data transformations that are often necessary in the modeling process. This versatility is crucial as it reflects the language's comprehensive approach to model interchangeability, making it a vital tool in the analytics and data science fields.

In contrast, options focusing solely on regression or predictive models limit the understanding of PMML’s capabilities. Such perceptions overlook the inclusion of descriptive analytics that can provide context and meaning to the data, which are also essential in the decision-making process. Thus, the comprehensive support for both predictive and descriptive models is what makes PMML a powerful asset for data scientists and analysts.