RapidMiner 9.7 is Now Available

Lots of amazing new improvements including true version control! Learn more about what's new here.

CLICK HERE TO DOWNLOAD

Automatic sampling_type in Split Operator

HeikoeWin786HeikoeWin786 Member Posts: 48 Contributor II
edited August 2 in Help
Hello all,

Just one quick question if anyone has any idea on this.
For the Automatic sampling_type in Split Operator, it is said that it will use stratified sampling if the label is nominal, shuffled sampling otherwise. 
What if the label is polynominal? It will be used stratified sampling?
Because I have imbalanced classes and I want to split the data as split 1: 75-25, then again split that 75 from split 1 into 75-25 an split 2.
I will save the model from split 2 and input the 25 from the split 1 as the unseen data to test the model.

thanks and regards,
Heikoe
Tagged:

Best Answer

  • hbajpaihbajpai Member Posts: 99   Unicorn
    Solution Accepted
    @HeikoeWin786

    The split data is robust to split more than 2 classes in a stratified manner, as long as you have non-numeric label. Also, I would recommend you try Cross Validation on your split 1 if possible and then use the 25% remaining data as your test(unseen) data. You might not need further splits in that case. Though, CV can take be compute and time intensive, but it is generally worth it. 

    There was a good thread on this in past as well.
    Best,
    Harshit
    lionelderkrikorHeikoeWin786
Sign In or Register to comment.