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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MartinLiebig

Hi,
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

t_liebe

The Problem is that the Attribute can't be evaluated easily.
Maybe I can explain my Goal with another example:

ID Blue Yellow Brown Green (Label) 1 1 1 0 1 2 0 1 0 0 3 1 0 0 0 4 1 1 1 0 5 0 1 1 0 6 1 0 1 0 ID Blue Yellow Green (Label) 1 1 1 50% 1; 50% 0 2 0 1 0 3 1 0 0

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

MartinLiebig

Hi,
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

Telcontar120

I agree, this seems like the case where a segmented scorecard based on underlying data availability would be the best solution. This is a fairly typical setup in my experience.