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Author Topic: Explanation of attribute types  (Read 18376 times)
Posts: 9

« on: October 23, 2008, 01:44:17 PM »


I'm new to Rapid Miner and currently searching for an explanation how to use the different attribute types like label, id, weight, batch, cluster, etc..
The Rapid Miner tutorial refers to the operator documentation. Unfortunately, there exists no such thing on the tutorial web page. Can anybody help me with this ?  Huh

Thanks in advance
Sebastian Land
Hero Member
Posts: 2426

« Reply #1 on: October 23, 2008, 03:47:35 PM »

Hi Werner,
its pretty simple:
Attributes contain information about your example. Some types of information are special, providing information not suitable to be used as learning input. This could for example be the real label, found by humans for this particular example. You dont want to use the real label as input variable for learning, otherwise the result will be pretty simple: Examples of Label A get Label A. So special attributes are not used for learning.
The type now defines their role:
 - The Id attribute is used for identifying examples
-  The label attribute is used to store the real label
- The weight is used to give an example a weight, if it is very important. Learner then will give this example more attention to predict this example correct.
- Cluster attribute stores the information which cluster this example had been assigned to
- prediction attributes will store a prediction performed by a model applier or something else.
The other special types are very...special and only used in a few applications. You might ignore for now.

Hope I could help,
  greetings Sebastian

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