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Apply Model with less Attributes
Hey guys,
given a working model, is it possible to apply the model on an example set with less/different attributes? I have a lot of data on which I can build the model. However, I want to make a prediction on data where this data is not yet available.
Example:
ID | Happy (Label) | Text | Legal Age |
1 | true | Lorem ipsum dolor sit amet, … | true |
2 | false | Lorem ipsum dolor sit amet, … | false |
3 | false | Lorem ipsum dolor sit amet, … | true |
4 | true | Lorem ipsum dolor sit amet, … | true |
5 | false | Lorem ipsum dolor sit amet, … | false |
As you can see, legal age correlates with the Label, but when I want to apply the model on data sets, I only have the texts. Is that possible ?
Thank you for your help.
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Answers
what should the model do in this case if it needs it? Some models can add missings here and evaluate like the Age would be missing.
BR,
Martin
Dortmund, Germany
The Problem is that the Attribute can't be evaluated easily.
Maybe I can explain my Goal with another example:
Although Attribute Brown is missing, you can still make a prediction based on the data set before. I know this might not be possible, but I thought it is worth a try to ask.
Kind regards,
Tobias
don't you want to built a second model without the missing attribute in and then select during application which model to take?
BR,
Martin
Dortmund, Germany
Lindon Ventures
Data Science Consulting from Certified RapidMiner Experts