is it pointless to stack models of different data types/structure? I am studying ensemble methods machine learning, in particular I am focusing on stacking.
In stacking different models are used to get an output. Then all the outputs are 'combined' together to build a model.
So, first of all, I guess the models MUST be different between each other (does it make sense?). Once we have these models, does it make sense to apply them to different data types/structures?
UPDATE: For “data structures” I am just saying different methods that learn different models (e.g. a degree 2 polynomial, a degree 4 polynomial, a KNN classifier, a SVM, etc.).

Using Wikipedia's definition

In computer science, a data structure is a data organization,
management, and storage format that enables efficient access and
modification. More precisely, a data structure is a collection of data
values, the relationships among them, and the functions or operations
that can be applied to the data, i.e., it is an algebraic structure
about data.

Calling things like "a degree 2 polynomial, a degree 4 polynomial, a KNN classifier, a SVM, etc." is unfortunate. Those are machine learning algorithms, they have nothing to do with data structures.
Machine learning model is a mathematical representation of the data, partially assumed (structure), partially learned from the data (parameters). Things such as linear regression, polynomial regression, $k$-NN, random forest, would all be refered as models. For learning the model we have machine learning algorithms. People would often use the terms exchangably.
By stacking models we mean stacking any kind of models. You could stack dofferent linear regression models, that differ by the data that was used for training them, and hence by the parameters, but also you can stack together $k$-NN, linear regression, SVM, and random forest. Usually, stacking diverse models gives better results than for homogeneus models, so it would be perfectly reasonable to stack together $k$-NN, SVM, and polynomial regression.
