Comments (2)
Bayesian optimization relies on regression models to model the response function. In the case of pure binary variables, the regression model does not makes sense.
Why do you use POI instead of the default EI?
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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
- 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
- Using BayesOpt as an library
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