Automated Machine Learning, which supports practitioners and researchers with
the tedious task of manually designing machine learning pipelines, has recently
achieved substantial success. In this paper we introduce new Automated Machine
Learning (AutoML) techniques motivated by our winning submission to the second
ChaLearn AutoML challenge, PoSH Auto-sklearn. For this, we extend Auto-sklearn
with a new, simpler meta-learning technique, improve its way of handling
iterative algorithms and enhance it with a successful bandit strategy for
budget allocation. Furthermore, we go one step further and study the design
space of AutoML itself and propose a solution towards truly hand-free AutoML.
Together, these changes give rise to the next generation of our AutoML system,
Auto-sklearn (2.0). We verify the improvement by these additions in a large
experimental study on 39 AutoML benchmark datasets and conclude the paper by
comparing to Auto-sklearn (1.0), reducing the regret by up to a factor of five