Bayesian Optimization Results Evaluation
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I am trying to learn and understand Bayesian Optimization. My code is working like in the documentation page but what is the difference between best observed feasible point and best estimated feasible point? Which result should I consider? Thanks for the help.
Alan Weiss 2018 年 5 月 24 日
The difference is that the algorithm makes a model of the objective function, and this model assumes that observations can contain noise (errors). So the best observed feasible point is the one with the lowest returned value from objective function evaluations. The best estimated feasible point is the one that has the lowest estimated mean value according to the latest model of the objective function.
If your objective function is deterministic, then you can set the 'IsObjectiveDeterministic' name-value pair to true, and then these two points are likely to coincide.
MATLAB mathematical toolbox documentation