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normalizing realtime data
Learner IHi,
how do I normalize realtime data in the same fashion as I normalized the training and test data to create the model?
after my model creation and validation i have normalized data but when i want to implement new data it is obviously not normalized and i cannot figure out how to normalize it so the scale is similiar. I hope you guys understand what I mean. If you have other suggestions how to solve this i am also open to listen.
I'm somewhat new to rapidminer so dont be mad if the answer might be obvious.
after my model creation and validation i have normalized data but when i want to implement new data it is obviously not normalized and i cannot figure out how to normalize it so the scale is similiar. I hope you guys understand what I mean. If you have other suggestions how to solve this i am also open to listen.
I'm somewhat new to rapidminer so dont be mad if the answer might be obvious.
-Knegge
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Answers
Look at the "pre" output of Normalize. This is a "preprocessing model". You can store it in the repository, retrieve it again and apply it using Apply Model. It remembers the normalization parameters for each attribute.
The most elegant way to do is, however, Group Models. Check out its tutorial process in the help, it illustrates how you use it.
In a cross validation you should do the normalization and the model building inside the left part of the Validation operator (training). Group Models helps here a lot because applying that grouped model becomes much easier. And as normalization is used for making the model better, it should also be validated.
Regards,
Balázs