Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

Understand

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.