reasons for getting different results
Greetings,
I'd like to thank you in advance for your help and efforts
I'm newbie to rapid miner so excuse me if my question was too simple
but I've encountered a problem with using the same dataset and process shared by a friend of mine,
I've not changed anything in the models used or parameters yet I get completely different results from her.
the process contains split validation with decision tree model.
Thank you.
I'd like to thank you in advance for your help and efforts
I'm newbie to rapid miner so excuse me if my question was too simple
but I've encountered a problem with using the same dataset and process shared by a friend of mine,
I've not changed anything in the models used or parameters yet I get completely different results from her.
the process contains split validation with decision tree model.
Thank you.
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@Haifa_G7
Hello
1) Is your detaset balance?
2) Do you have any single label in your dataset?
3) Also for split validation, did you and your friend use the same for train and test part of dataset?
Regards
mbs
Hello
1) Is your detaset balance?
2) Do you have any single label in your dataset?
3) Also for split validation, did you and your friend use the same for train and test part of dataset?
Regards
mbs
Altair Employee
Can you check if the "local random seed" parameter in split validation operator is set? That might be one reason as test and train data might differ between both of you. If you could post the process here, we can check it. You can attach .rmp file here.