At a high level, the process works as follows.
- The Iris data set is used with weights being produced using "Weight By Information Gain"
- These weights are transformed into an example set and stored for later use inside a Loop operator
- A subprocess is used to make sure everything works in the right order (this technique is also used inside the Loop).
- A "Loop Attributes" operator iterates over all attributes and generates a new attribute based on multiplying the existing value by a weight. The attribute name is required to be contained in the weights example set.
- The weight for each example is calculated with a combination of filtering and macro extraction.