[英]what is the kappa variable (BayesianOptimization)
I read some posts and tutorials about BayesianOptimization
and I never saw explanation about kappa
variable.我阅读了一些有关BayesianOptimization
的帖子和教程,但从未看到有关kappa
变量的解释。
kappa
variable?什么是kappa
变量?BayesianOptimization
process?这些值如何影响BayesianOptimization
过程? The kappa
parameter, along with xi
, is used to control how much the Bayesian optimization acquisition function balances exploration and exploitation. kappa
参数与xi
一起用于控制贝叶斯优化获取 function 平衡探索和利用的程度。
Higher kappa
values mean more exploration and less exploitation and vice versa for low values.较高的kappa
值意味着更多的探索和更少的开发,反之亦然。 Exploration pushes the search towards unexplored regions and exploitation focuses on results in the vicinity of the current best results by penalizing for higher variance values.探索将搜索推向未探索的区域,而利用则通过惩罚更高的方差值来关注当前最佳结果附近的结果。
It may be beneficial to begin with default kappa
values at the start of optimization and then lower values if you reduce the search space.在优化开始时使用默认kappa
值可能会有所帮助,然后如果您减少搜索空间,则使用较低的值。
In scikit-optimize, kappa
is only used if the acquisition function acq_func
is set to “LCB” and xi
is used when acq_func
is “EI” or “PI” where LCB is Lower Confidence Bound, EI is Expected Improvement and PI is Probability of Improvement.在 scikit-optimize 中,仅当采集 function acq_func
设置为“LCB”时才使用kappa
,并且当acq_func
为“EI”或“PI”时使用xi
,其中 LCB 是置信下限,EI 是预期改进,PI 是概率改进。
Similarly for the BayesianOptimization package:同样对于贝叶斯优化 package:
acq: {'ucb', 'ei', 'poi'}
The acquisition method used.
* 'ucb' stands for the Upper Confidence Bounds method
* 'ei' is the Expected Improvement method
* 'poi' is the Probability Of Improvement criterion.
Mathematical details on acquisition functions 采集函数的数学细节
Note, the BayesianOptimization package and scikit-optimize use different default kappa
values: 2.576 and 1.96 respectively.请注意, 贝叶斯优化 package 和scikit-optimize使用不同的默认kappa
值:分别为 2.576 和 1.96。
There is a decent exploration vs exploitation example in the scikit-optimize docs . scikit-optimize 文档中有一个不错的探索与利用示例。
There is a similar BayesianOptimization exploration vs exploitation example notebook .有一个类似的贝叶斯优化探索与利用示例笔记本。
FWIW I've used both packages and gotten OK results. FWIW 我已经使用了这两个软件包并获得了不错的结果。 I find the scikit-optimize plotting functions to be useful when fine tuning the parameter search space.我发现 scikit-optimize 绘图函数在微调参数搜索空间时很有用。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.