Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge-and labor-intensive to pursue good learning performance, human experts are heavily involved in every aspect of machine learning. In order to make machine learning techniques easier to apply and reduce the demand for experienced human experts, automated machine learning (AutoML) has emerged as a hot topic with both industrial and academic interest. In this paper, we provide an up to date survey on AutoML. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers most existing approaches to date, but also can guide the design for new methods. Subsequently, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques. Finally, we provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research. Index Terms-automated machine learning,neural architecture search,hyper-parameter optimization,meta-learning,transfer-learning ! The first example is Auto-sklearn [7] . As different classifiers are applicable to different learning problems [1], [13] , it is natural to try a collection of classifiers on a new problem, and then construct a final prediction from them. However, setting up classifiers and their hyper-parameters is a tedious task, which usually requires human involvement. Based on the popular scikit-learn machine learning library [14], Autosklearn can automatically find good models from some
Recent years have witnessed the advance of Automated Machine Learning (AutoML) REF .
Taking Human out of Learning Applications: A Survey on Automated Machine Learning
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