Comments (1)
BayesOpt always assume that the function is noisy. Also, it might imply that your model is not suitable for your problem and have to tune the parameters.
Note that BayesOpt can work with discrete spaces, but it's better/easier for continuous.
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Related Issues (20)
- Python 3 support HOT 3
- Multicore processing HOT 1
- function evaluation out of range HOT 1
- compatibility with python3 HOT 2
- Use BayesOPT to optimize categorical variables HOT 2
- About mSigma (variance) in gaussian_process.cpp HOT 1
- Build error on Ubuntu 17.04 HOT 4
- Why it doesn't converge to the right value HOT 1
- noise effect HOT 1
- Compatibility with python 3.6 HOT 1
- Access to the surrogate model through C API? HOT 2
- Issue in MATLAB compilation HOT 1
- center of search space HOT 1
- example: Build + Install on Google Colabratory w/ Python 3.6 HOT 2
- Segfault for low discrete parameter space HOT 1
- MultiObjective optimization HOT 1
- [Request] Add a CITATION.cff file HOT 1
- Build Fails for BAYESOPT_BUILD_SHARED=ON HOT 2
- Utilise Boost to provide some sort of clue for unknown errors HOT 1
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