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This repository has been archived by the owner on Feb 23, 2023. It is now read-only.
Hi,
I don't know the difference between normalize_Y and exact_feval in GpyOpt.
I see the documention like:
Is it means that we only have to set one of the parameters? (for example, if we choose normalize_Y=True, we don't have to set exact_feval=False) Could you help me explain it?
Thank you!
The text was updated successfully, but these errors were encountered:
Base on my understanding, they are two different things:
normalize_Y means it will normalize the Y data before doing optimization. But user will always get dimensional output Y. Unless you debug into the detailed optimization processes, you will not interactive with the normalized Y.
Exact_feval means if your evaluation of the objective function f is exact. For example,
if f_math has a know mathematical formula, usually extact_feval=True unless you contaminate the evaluation of f.
if f_measure is some a measurement of some physical quantify like temperature, you should gain inexact f_measure and thus extact_feval=False, unless you want to treat the measurement as truth and assume no noise in it.
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Hi,
I don't know the difference between
normalize_Y
andexact_feval
in GpyOpt.I see the documention like:
Is it means that we only have to set one of the parameters? (for example, if we choose normalize_Y=True, we don't have to set exact_feval=False) Could you help me explain it?
Thank you!
The text was updated successfully, but these errors were encountered: