🥳 RAPIDMINER 9.9 IS OUT!!! 🥳
The updates in 9.9 power advanced use cases and offer productivity enhancements for users who prefer to code.
Need help with example based costs sensitive classification
I have an example set (saved ARFF file) where one attribute represents data that I use solely to calculate the costs of misclassifying that example. My learner is really a meta leaner and uses arbitrary inner learners (standard Rapid Miner learners) for the actual classification.
Not surprisingly, when I submit the data to the learner, it uses that attribute for classifying examples (not desired). I have figured out that giving that attribute the "weight" special designation will cause the average learner to ignore my special attribute it unless it specifically uses weights. For many standard rapid miner learners, I can usually de-select the "use weights" parameter to prevent this. However, I've discovered that some learners use weights and do not always allow me to turn that off. So I'm looking for a more general solution.
Is there an alternative to using the "weight" designation in order to specify an attribute as a "special attribute" that the average learner will ignore?
Any help would be greatly appreciated. Thanks!