Problem: Design problem

parameters consist of the search space of your model.

Scientists design experiments to gain insights into physical and social phenomena.

All these design problems are fraught with choices, choices that are often complex and high dimensional, with interactions that make them difficult for individuals to reason about.

When a data scientist uses a machine learning library to forecast energy demand, we would like to automate the process of choosing the best forecasting technique and its associated parameters.

Automated design. 

Bayesian optimization has emerged as a powerful solution for these varied design problems.

Supplementary knowledge:

1. 贝叶斯优化: 一种更好的超参数调优方式

https://zhuanlan.zhihu.com/p/29779000

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