The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
Combining Multiple Imputation in Rapid miner
faridehbagherza
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!
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!
0
Answers
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