How do I create these layers (image) with stacking?

MarlaBot

@Jack530 wants to know the answer to this question.

"Hi. My goal is to create a highly accurate linear regression model. Such as the image in the link displays (logistic regression in the image). My question is how, probably with the stacking operator, do i create these layers? I now start with read cvs (training set with al corresponding roles/labels), then how do you move to a the model in the picture and eventually a highly accurate model (being aware of cross-validation/optimising parameters and all other important operators).

These are the steps that should be taken according to the image, and am stuck on how to implement in RapidMiner:
1. A diverse set of models with an assembled result
2. Subsequently the output of 1. is the input this phase; assembling models (bagging?)
3. Lastly the output of 2. is the input of the eventual model (so built on the predictions of al preliminary models).

I would greatly appreciate advice."

Telcontar120

This is an incredibly complicated architecture.  My interpretation of the diagram is that there are 64 separate individual models and 15 ensemble models in stage 1, all of which are built separately.   Maybe they are built on different segments of the underlying data (e.g., a segmented scorecard approach?  it is impossible to tell from the diagram).  These predictions are then passed into stage 2, which has 2 ensemble models built off them.  This results in another set of predictions, which are passed into a single LR in stage 3.

This is all completely do-able in RapidMiner, albeit very tedious.  Is there a definite need to replicate this same structure?  You say you want to create a "highly accurate" model but there is no guarantee that the final model in this architecture is significantly more accurate than a single LR model---that totally depends on the data.  
I would suggest you start by building a single LR model and assessing its performance against some other popular ML algorithms like k-nn or GBT.  This is easy to do with AutoModel.  You might then consider a simpler ensemble solution using a Voting or Stacking operator.  But there seems like no need to go directly to the layout depicted in this diagram.