image

🎉 🎉 RAPIDMINER 9.10 IS OUT!!! 🎉🎉

Download the latest version helping analytics teams accelerate time-to-value for streaming and IIOT use cases.

CLICK HERE TO DOWNLOAD

Combining Multiple Imputation in Rapid miner

faridehbagherzafaridehbagherza Member Posts: 22 Contributor II
Hi! I used R for multiple imputation and imputed 5 Imputations of my data. For the Model, I am using a stacking model of 3 base learners.
I don`t know what I should do with these imputations of the data. Should I train all my base learners with all these imputations individually?
That sounds right, but it takes a lot of time to train each of the base learners with each of the imputed data sets and then again train the stacked model with each of the imputed data sets!
Anyway, if that`s right, how can I combine the five models learned by 5 imputed data sets?
I mean, for example, to combine models for a stacking model, or addaboost or ... there are operators, but to combine models built from different imputed data sets, I couldn`t find any operator!

Answers

  • faridehbagherzafaridehbagherza Member Posts: 22 Contributor II
    Here is a sample of what I was talking about:
    I uploaded 2 codes on pastebin.com

    1st code: http://pastebin.com/vjr8p9a7
    2nd code: http://pastebin.com/Zn0aduu5

    Here is a little explanation about them: 1. You need to have VIM package of R for being able to run it!
    2. I upload two codes for you! In the first one I just imputed 1 dataset, and in the second one I imputed 5 datasets.
    About the first code: Here, in the first Subprocess I trained 3 base learners and in the second subprocess I used these 3 learners for training a stacking model!
    The stacking model has a better performance of all!
    About the second code:Here in the first subprocess, I used 5 imputations to train 5 stacking models just like how I did in the first code! Then in the second subprocess I voted on these 5 models built by 5 imputations to combine the results to gain better performance!
    I hope you don`t get confused with the process!
    Any suggestions on the whole process would be welcomed!
    I mean any other way to combine the results of the imputations instead of voting or ...!
    In these processes I trained all the base learners with all the imputations, is that the common way?
    Thanks in advance.
    Regards
    Farideh
  • Marco_BoeckMarco_Boeck Team Lead Software Engineering Administrator, Moderator, Employee, Member, University Professor Posts: 1,963   RM Engineering
    Hi,

    please do not post the same topic asking for help all over the forums. If anything, it will get your help slower. Thread continues here: http://rapid-i.com/rapidforum/index.php/topic,6983.msg24400.html

    Regards,
    Marco
Sign In or Register to comment.