stacking operator

Thiru

dear all,  For my classification problem using supervised learning, Im trying to use stacking operator. 
The tutorial for this operator shows two layer, i.e. one base  learner layer + stacking  learner.   
My query is , can we have more than two layers...?
For example,  is it possible to have 3 layers,  i.e.  two layers of base learner , before I fed to the stacking learner?  
If so, how to configure that?  kindly let me know. thanks.

thanx
thiru

Telcontar120

Can't you just put one stacking operator inside another?  So your "base learner" of the outer stack would be another stacking operator of its own?
Or in theory you could use any other ensemble operator as well.  Basically anything that gives you a model output which is all that the stacking operator requires as an input on the left side. 

Telcontar120

Can't you just put one stacking operator inside another?  So your "base learner" of the outer stack would be another stacking operator of its own?
Or in theory you could use any other ensemble operator as well.  Basically anything that gives you a model output which is all that the stacking operator requires as an input on the left side. 

Thiru

thanks for your reply.  yes , it works. 

1.  Is there any guideline how to combine the operators. 
Using Gradient boost alone gives  accuracy 75.75%.  I want to take it beyond 95% using stacking.
 (ofcourse I'll look into precision/recall). 

 I tried many stacking combinations,  i couldnt make it beyond 73%.

2. Any theoretical/white paper reference / rapidminer resource/ book on how to choose the  combination of  base learners , stack learner  for the maximum classification performance.  

3.  will it be useful to use non-neural net operator as base learner & neural net (deep learning ) as stacking learner?
i tried this combination, in my case, it reduces the performance to 55%.
  
thank you

thiru