Due to recent updates, all users are required to create an Altair One account to login to the RapidMiner community. Click the Register button to create your account using the same email that you have previously used to login to the RapidMiner community. This will ensure that any previously created content will be synced to your Altair One account. Once you login, you will be asked to provide a username that identifies you to other Community users. Email us at Community with questions.

Attribute weight help

PPPP Member Posts: 9 Contributor II
edited November 2018 in Help
Hi,
I'm working with decision trees, I’m trying to understand the factors must important that conducts to a sewer pipe failure, my records have attributes like diameter , length, etc. I have an attribute whit the number of failures in that pipe, I think this information can be use like a weight. Because the data is unbalanced most of the examples have a value 0. If I use “set rule” to set this attribute as weight, the tree becomes completely trained and useless, so I have to get out this attribute to get results. So my question is, is there a way to use this information without over training the tree.

Thanks for your attention,
Paulo Praça
Tagged:

Answers

  • MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,529 RM Data Scientist
    Have you considered to put the role of that attribute to weight?

    ~Martin
    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
  • PPPP Member Posts: 9 Contributor II
    yes I put, but the tree get overtraining, when I did that the tree only have two leaves. The failure range frequency varies between 1 and 6, maximum i have 6 failures in one pipe but for almost every one I have only one failure. IF I put this attribute as ‘weight’ he shadows the others attributes.
  • MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,529 RM Data Scientist
    I think we have a different understanding of overtraining. I guess your tree simply gets worse by this.

    Have you tried to change the minimal gain?
    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
  • JEdwardJEdward RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 578 Unicorn
    What if you discretized that field and used it as the label? 

    For example:
    Faults
    0
    1
    2-3
    4-5
    6+

    Or even more simply Faults: Low, Medium, High. 
Sign In or Register to comment.