AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. They often combine techniques from many different sub-fields of machine learning in order to find a model or set of models that optimize a user-supplied criterion, such as predictive performance. The ultimate goal of such systems is to reduce the amount of time spent on menial tasks, or tasks that can be solved better by algorithms while leaving decisions that require human intelligence to the end-user. In recent years, the importance of other criteria, such as fairness and interpretability, and many others have become more and more apparent. Current AutoML frameworks either do not allow to optimize such secondary criteria or only do so by limiting the system's choice of models and preprocessing steps. We propose to optimize additional criteria defined by the user directly to guide the search towards an optimal machine learning pipeline. In order to demonstrate the need and usefulness of our approach, we provide a simple multi-criteria AutoML system and showcase an exemplary application.
翻译:自动ML系统目前越来越受欢迎,因为它们可以在没有人类监督的情况下建立强大的模型,它们往往将来自机器学习的许多不同次领域的技术结合起来,以便找到一种模型或一套模型,优化用户提供的标准,例如预测性性能等。这种系统的最终目标是减少用算法花费的时间,或减少可以通过计算方法更好地解决的任务,同时将需要人类情报的决定留给最终用户。近年来,其他标准,例如公平和可解释性,以及许多其他标准的重要性越来越明显。目前的自动ML框架要么不允许优化这种次级标准,要么只允许通过限制系统对模式的选择和预处理步骤来这样做。我们建议优化用户确定的额外标准,直接指导搜索工作走向最佳机器学习管道。为了证明我们的方法的必要性和效用,我们提供了一个简单的多标准自动ML系统,并展示一个模范的应用。