论文标题
调整机器学习算法的超参数的重要性
Importance of Tuning Hyperparameters of Machine Learning Algorithms
论文作者
论文摘要
许多机器学习算法的性能取决于其超参数设置。这项研究的目的是确定调整超参数是否重要，还是可以安全地将其设置为默认值。我们提出了一种方法，以确定基于非效率测试和调整风险的高参数调整高参数的重要性：当未调整超参数时会产生的性能损失，而是设置为默认值。由于我们的方法需要默认参数的概念，因此我们提出了一个简单的过程，该过程可用于确定合理的默认参数。我们使用OpenML的59个数据集应用了基准研究中的方法。我们的结果表明，将特定的超参数以其默认值不属于调整这些超参数。在某些情况下，将超级参数以默认值为单位，甚至胜过使用有限数量的迭代的搜索过程对其进行调整。
The performance of many machine learning algorithms depends on their hyperparameter settings. The goal of this study is to determine whether it is important to tune a hyperparameter or whether it can be safely set to a default value. We present a methodology to determine the importance of tuning a hyperparameter based on a non-inferiority test and tuning risk: the performance loss that is incurred when a hyperparameter is not tuned, but set to a default value. Because our methods require the notion of a default parameter, we present a simple procedure that can be used to determine reasonable default parameters. We apply our methods in a benchmark study using 59 datasets from OpenML. Our results show that leaving particular hyperparameters at their default value is non-inferior to tuning these hyperparameters. In some cases, leaving the hyperparameter at its default value even outperforms tuning it using a search procedure with a limited number of iterations.
