  Automated Machine Learning (AutoML) supports practitioners and researchers
with the tedious task of designing machine learning pipelines and has recently
achieved substantial success. In this paper, we introduce new AutoML approaches
motivated by our winning submission to the second ChaLearn AutoML challenge. We
develop PoSH Auto-sklearn, which enables AutoML systems to work well on large
datasets under rigid time limits by using a new, simple and meta-feature-free
meta-learning technique and by employing a successful bandit strategy for
budget allocation. However, PoSH Auto-sklearn introduces even more ways of
running AutoML and might make it harder for users to set it up correctly.
Therefore, we also go one step further and study the design space of AutoML
itself, proposing a solution towards truly hands-free AutoML. Together, these
changes give rise to the next generation of our AutoML system, Auto-sklearn
2.0. We verify the improvements by these additions in an extensive experimental
study on 39 AutoML benchmark datasets. We conclude the paper by comparing to
other popular AutoML frameworks and Auto-sklearn 1.0, reducing the relative
error by up to a factor of 4.5, and yielding a performance in 10 minutes that
is substantially better than what Auto-sklearn 1.0 achieves within an hour.

Machine Learning;Machine Learning