With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are good priors over their hyperparameters' ranges to draw values from? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. Our results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. Our experiments confirm that the selected hyperparameters are indeed the most important ones and that our obtained priors also lead to improvements in hyperparameter optimization.

通过在许多数据集上进行元学习，本文提出了一种基于元分析进行自动超参数重要性分析和先验推导的方法，并将其应用于支持向量机、随机森林和 Adaboost 算法中，得到的结果可在手动算法设计和自动超参数优化方面提供数量基础。

跨数据集的超参数重要性