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Setting the whole matrix dataset as a label
Contributor II
Let's say you have two-time series datasets, the first dataset has five explanatory variables, and the other one is a dependent dataset with about 1000 variables, each representing an individual cell. So how do you set the whole dependent dataset as a label for prediction or regression analysis?
If not possible, is it possible to loop the whole dataset and perform predictions for each cell?
If not possible, is it possible to loop the whole dataset and perform predictions for each cell?
0
Answers
In RapidMiner, one attribute is the label. It is marked as such using the Set Role operator.
You can use Loop Attributes to apply the same process on the different attributes. I guess you would loop on the dependent dataset's attributes, select the current attribute (identified by the loop macro), and join it to the explanatory variables. Then you'll have a model for each dependent attribute which you can store in your repository using Store and apply later.
Regards,
Balázs
Take a look at the operator "Multi Label Modeling" for time series
https://docs.rapidminer.com/9.9/studio/operators/modeling/predictive/ensembles/multi_label_model_learner.html
Basically this is doing some auto-loop for different prediction target, usually defined for time series forecasting
Hope it helps!
Cheers,
YY