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"PCA vs PrincipalComponentGenerator?"
From what I could see, experiments ExampleSource-PrincipalComponentsGenerator(1) and ExampleSource-PCA-ModelApplier(2) generate the same output data sets in the input set contains a label attribute. If the input does not have a label, experiment (1) crashes at runtime, even though it passes validation. In addition, the experiment (2) outputs the PCA model, and has more controls (number of PCs).
If the PCA operator is clearly superior to the PrincipalComponentsGenerator, why do you keep the PrincipalComponentsGenerator? Or does it have any advantages I missed?