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Examining cause for mislabeling

b00122599b00122599 Member Posts: 12 Newbie
Hey folks,

I have completed a project and have an output for a decision tree predicting 4 classes. One of the classes I have a class precision of 99% then 69%, 66% and finally only 54% for my last class. What I want to do now is go through the validated example set outputted from the decision tree and examine which labels where incorrectly labeled, and what attributes are influencing the mislabeling. Is there any process in rapidminer that would be useful for this or would it be more of a manual job?

Thanks in advance,

Neil. 

Best Answer

Answers

  • b00122599b00122599 Member Posts: 12 Newbie
    Thank you very much Varun!
    varunm1
  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,257   Unicorn
    You can also filter your dataset for the incorrect predictions very easily if you want to profile those examples or examine them in more detail.  It is one of the built-in options in the Filter Examples operator (check the drop down parameter).

    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
    sgenzer
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