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Author Topic: [SOLVED] creating very many models using a single data set?  (Read 615 times)
np1234
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« on: August 01, 2013, 05:33:08 PM »

Hi,

I'm a new user of RapidMiner and was wondering if someone in the community knows a good way to create what I am trying. 

I am working with a very large dataset (millions of examples) that has 1 id attribute, 1 text attribute, 52 numerical attributes per example (row) and 1 label attribute.  There are about 500 unique text attributes in the whole data set.  What I would like to do is create a decision tree model (and store it) for data corresponding to each unique text attribute.  That is, for each unique text attribute, I want all the examples corresponding to that text attribute and then train a decision tree model using the 52 numerical and 1 label attributes.  I could do it using filter examples, decision tree model, and repository store operators manually for each unique text attribute, but I would have to do this about 500 times.  Is there an efficient way to implement this?  I could try to do this using scripting, but I was just wondering if I could use the built in operators.   Is the Loop operator the answer?

Thanks in advance.
« Last Edit: August 29, 2013, 10:01:52 AM by David A » Logged
David A
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« Reply #1 on: August 22, 2013, 11:31:04 AM »

Hi,

the Loop Values operator is the one you are looking for.
The tutorial process shows a very similar example to your problem, using the value of the loop_value macro to filter the examples.
I hope this answere your questions, if not don't hesitate to ask.

Best regards,
David
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np1234
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« Reply #2 on: August 23, 2013, 07:01:13 PM »

Thanks David, I did figure it out a couple weeks ago using just as you said, the operator, Loop Value.  It took me a little bit to how to access the macro, but got it finally.  The curly brackets threw me off.  I thought they were used to imply the macro name, but they actually had to be put in there.
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