There are currently many barriers that prevent non-experts from exploiting machine learning solutions ranging from the lack of intuition on statistical learning techniques to the trickiness of hyperparameter tuning. Such barriers have led to an explosion of interest in automated machine learning (AutoML), whereby an off-the-shelf system can take care of many of the steps for end-users without the need for expertise in machine learning. This paper presents Ensemble Squared (Ensemble$^2$), an AutoML system that ensembles the results of state-of-the-art open-source AutoML systems. Ensemble$^2$ exploits the diversity of existing AutoML systems by leveraging the differences in their model search space and heuristics. Empirically, we show that diversity of each AutoML system is sufficient to justify ensembling at the AutoML system level. In demonstrating this, we also establish new state-of-the-art AutoML results on the OpenML tabular classification benchmark.
翻译:目前有许多障碍阻碍非专家利用机器学习解决方案,从缺乏对统计学习技术的直觉到超参数调试的诡计,这些障碍导致对自动机器学习(Automle)的兴趣激增,使现成的系统能够照顾最终用户的许多步骤,而不需要机械学习方面的专业知识。本文展示了“组合广场”(Emberble$2$2$),这是一个自动ML系统,综合了最新开放源码自动ML系统的结果。“组合$2$2”利用了现有自动ML系统的多样性,利用了模型搜索空间和超常性的差异。我们生动地表明,每个自动ML系统的多样性足以证明在自动ML系统一级进行整合是正当的。为了证明这一点,我们还在OpenML表格分类基准上建立了新的“最新”的自动ML结果。