What was the main purpose of the Predictive Model Markup Language (PMML)?

The main purpose of the Predictive Model Markup Language (PMML) is indeed to describe predictive models and data transformations. PMML serves as a standard format for representing statistical and data mining models, allowing these models to be shared and reused across different platforms and applications. This capability is crucial in environments where predictive analytics is integral to decision-making processes.

By including both model descriptions and data transformations, PMML ensures that a comprehensive view of the analytical workflow is captured. This means that not only is the model itself represented, but also the way data is pre-processed or transformed before it is input into the model. Such a holistic representation is essential for maintaining the integrity and reproducibility of predictive analytics within a business context.

The other options focus either on specific audiences or limited functionalities of PMML. They do not capture the full breadth of what PMML entails, particularly its strength in facilitating both model specifications and the necessary data transformations that occur prior to modeling.