论文标题
高参数对于机器学习算法的重要性
Hyperparameter Importance for Machine Learning Algorithms
论文作者
论文摘要
超参数在拟合监督机器学习算法的拟合中起着至关重要的作用。但是，计算在计算上昂贵的调音同时调整所有可调的超参数，尤其是对于大型数据集。在本文中，我们给出了可以通过亚采样程序估算的超级参数重要性的定义。根据重要性，可以更有效地对整个数据集进行超参数调整。从理论上讲，我们对数据子集的提议重要性与在弱条件下的人群数据上的重要性一致。数值实验表明，提出的重要性是一致的，可以节省大量计算资源。
Hyperparameter plays an essential role in the fitting of supervised machine learning algorithms. However, it is computationally expensive to tune all the tunable hyperparameters simultaneously especially for large data sets. In this paper, we give a definition of hyperparameter importance that can be estimated by subsampling procedures. According to the importance, hyperparameters can then be tuned on the entire data set more efficiently. We show theoretically that the proposed importance on subsets of data is consistent with the one on the population data under weak conditions. Numerical experiments show that the proposed importance is consistent and can save a lot of computational resources.
