Using optimize parameters(Grid) on decision tree
Hi,
I used optimize parameters(Grid) to determine the optimal parameters used in decision tree. The result give me something like maximal_depth=14, minimal leaf size=92. But when i look in the model, i found that there are some leafs containing only 1 sample. Do anyone know if i did something wrong?
I used optimize parameters(Grid) to determine the optimal parameters used in decision tree. The result give me something like maximal_depth=14, minimal leaf size=92. But when i look in the model, i found that there are some leafs containing only 1 sample. Do anyone know if i did something wrong?
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MartinLiebig
Altair EmployeeAccepted Answer
Hi @Ben_Suen,
did you use a validation scheme like cross-validation? This sounds a bit over-trained.
Best,
Martin
New Altair Community Member
Also did you actually optimize minimal leaf size parameter or minimal size for split? They work a bit differently.
New Altair Community Member
OPHi @Telcontar120
I optimized minimal leaf size parameter. i think there should be at least 92 sample in each leaf after the optimize parameter operator give me these parameter. Do i misunderstand something here?
I optimized minimal leaf size parameter. i think there should be at least 92 sample in each leaf after the optimize parameter operator give me these parameter. Do i misunderstand something here?
New Altair Community Member
@Ben_Suen can you post your process xml for us to review? I think @mschmitz whether or not the model is overtuned (although I agree with you) is a somewhat separate issue. If the tree is resulting in notes that are smaller than the minimal leaf size parameters it sounds like it could be a bug.
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Altair EmployeeAccepted Answer
Hi @Ben_Suen,
did you use a validation scheme like cross-validation? This sounds a bit over-trained.
Best,
Martin