基于贝叶斯优化的核岭回归超参数优化
Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization
摘要：
机器学习方法通常依赖于内部参数-所谓的超参数-需要优化以获得最佳性能。这种优化给机器学习从业者带来了负担，需要专业知识、直觉或计算上要求苛刻的蛮力参数搜索。在这里，我们解决了需要更有效的，自动化的超参数选择与贝叶斯优化。我们将这种技术应用于核岭回归机器学习方法，用于有机分子原子结构的两种不同描述符，其中一种将其自己的超参数集引入该方法。我们确定最佳的超参数配置和推断整个预测误差景观超参数空间，作为超参数依赖的视觉指南。我们进一步证明，对于越来越多的超参数，贝叶斯优化在计算时间上比穷举网格搜索（当前默认的标准超参数搜索方法）更有效，同时提供等同甚至更好的准确性。
英文摘要：
Machine learning methods usually depend on internal parameters -- so called hyperparameters -- that need to be optimized for best performance. Such optimization poses a burden on machine learning practitioners, requiring expert knowledge, intuition or computationally demanding brute-force parameter searches. We here address the need for more efficient, automated hyperparameter selection with Bayesian optimization. We apply this technique to the kernel ridge regression machine learning method for two different descriptors for the atomic structure of organic molecules, one of which introduces its own set of hyperparameters to the method. We identify optimal hyperparameter configurations and infer entire prediction error landscapes in hyperparameter space, that serve as visual guides for the hyperparameter dependence. We further demonstrate that for an increasing number of hyperparameters, Bayesian optimization becomes significantly more efficient in computational time than an exhaustive grid search -- the current default standard hyperparameter search method -- while delivering an equivalent or even better accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
