What kind of transformations can PMML handle?

The correct response indicates that PMML (Predictive Model Markup Language) is capable of handling both data transformations and model scoring. PMML is designed to provide a standard way to represent predictive models and their associated transformations in a way that is portable and can be understood across different systems.

Data transformations refer to the processes applied to input data to prepare it for scoring, which may include normalization, binning, or any other preprocessing techniques necessary to enhance the performance of the model. Additionally, PMML is used for scoring, which involves the application of predictive models to a dataset in order to generate outputs based on given inputs.

Therefore, PMML's ability to process data transformations alongside model scoring allows organizations to implement predictive models effectively and consistently across various environments, thereby improving decision-making processes.

The other options do not encompass the full range of capabilities offered by PMML. For instance, stating that PMML handles only structured data transformations ignores its capability of scoring as well. Predictive data transformations alone do not cover the entire scope of what PMML achieves, as it also includes scoring. Similarly, visual data transformations are not relevant in the context of PMML, which focuses on model and data handling rather than visual representation.