How to handle error on Example Set label value in Random Forest

Srp2023
Srp2023 New Altair Community Member
edited November 5 in Community Q&A
Hello I am developing model using Radom Forest. I am very new to this. The error I have says "The learning scheme random forest does not have sufficient capabilities for handling an example set with only one label value."
I do have only one label value though there are 7 other attributes in my 6000 example data set. I believe this is allowed. What could be the error.

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Answers

  • jwpfau
    jwpfau New Altair Community Member
    Hi,

    the problem is that all your examples have the same label value, i.e. "yes". 
    A regular Random Forest can't handle that, there are some approaches of generating artificial outliers beforehand.

    I.e. in Désir, C., Bernard, S., Petitjean, C., Heutte, L. (2012). A New Random Forest Method for One-Class Classification. In: , et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_31

    You could try a one-class SVM instead or try to get data that contains outliers.

    Greetings,
    Jonas
  • Srp2023
    Srp2023 New Altair Community Member
    Thank you for the reply, Jonas. My label is called Recommended IND and it has two values. 0 & 1. Screenshot attached. Could something else be the problem?




  • jwpfau
    jwpfau New Altair Community Member
    Hi,

    If you are using Cross Validation, can you change it to Stratified Sampling?

    Best would be if you could share your process.

    Greetings,
    Jonas
  • Srp2023
    Srp2023 New Altair Community Member
    Thank you for taking the time. 
  • jwpfau
    jwpfau New Altair Community Member
    Hi,

    in the Sample Operator you have written a lowercase "o" instead of 0.

    Greetings,
    Jonas
  • Srp2023
    Srp2023 New Altair Community Member
    OMG! Thank you Jonas. Embarassing as it is! I did not catch that in spite of looking over 20 times. Much appreciate it.