Typically in Rapidminer, we can save the resulting models from algorithms by using a simple "Store" operator.
We can then double-click those models and see their contents and we can also drop them into a new process and use them to score new data.
We can do this also when these models have been built in R or Python with the only difference that we cannot see inside the model artifacts. For example, if we drop and double-click a model build in Python or R, we get a fileobject without much description.
However, the model is there and can be used to score data without re-training.
See the example below:
Here, we have a gradient boosted trees model built in python and saved to the repository.
When trying to preview it, we get this:
However, if we build another process where we bring the model artifact in and apply it to data, it works!