Which component is NOT included in the common components of PMML?

The common components of Predictive Model Markup Language (PMML) include a variety of elements that help define the structure, the data needed for modeling, and the transformation of that data. The Header provides meta-information about the model, including details such as the model's name and version. The Data Dictionary specifies the data fields that are used in the model, defining the types of data and potential value ranges.

Data Transformation deals with the preprocessing steps applied to the data before it is input into a model, which is essential for ensuring that the data is in the right format and grounded in the right context. Each of these components plays a crucial role in ensuring that the model is well-defined and that the input data is understood when the model is executed.

In contrast, the Machine Learning Algorithm itself is not typically a component defined within PMML. Instead, PMML focuses on the representation of the model and its parameters rather than including the algorithms that underpin them. The essence of PMML is to provide a standard for model interchange, allowing for the expression of various statistical and data mining models without being tied to a specific implementation of the machine learning algorithm. This distinction makes it clear why C is the correct answer, as it is not a component explicitly included in