Best way to handle imbalanced dataset for multi-class classification in Auto-Sklearn
For those interested and as an addition to the answers given, I can highly recommend the following paper: 
Lemnaru, C., & Potolea, R. (2011, June). Imbalanced classification problems: systematic study, issues and best practices. In International Conference on Enterprise Information Systems (pp. 35-50). Springer, Berlin, Heidelberg.
The authors argue that: 

In terms of solutions, since the performance is not expected to improve
  significantly with a more sophisticated sampling strategy, more focus should be
  allocated to algorithm related improvements, rather than to data improvements.

As e.g. the ChaLearn AutoML Challenge 2015 used the balanced accuracy, sklearn argues that it is a fitting metric for imbalanced data and Auto-Sklearn was able to compute well-fitting models, I'm going to have a try. Even without resampling, the results were much "better" (in terms of prediction quality) than just using the accuracy.
I'm using Auto-Sklearn and have a dataset with 42 classes that are heavily imbalanced. What is the best way to handle this imbalance? As far as I know, two approaches to handle imbalanced data within machine learning exist. Either using a resampling mechanism such as over- or under-sampling (or a combination of both) or to solve it on an algorithmic-level by choosing an inductive bias that would require in-depth knowledge about the algorithms used within Auto-Sklearn. I'm not quite sure on how to handle this problem. Is it anyhow possible to solve the imbalance directly within Auto-Sklearn or do I need to use resampling strategies as offered by e.g. imbalanced-learn? Which evaluation metric should be used after the models have been computed? The roc_auc_score for multiple classes is available since sklearn==0.22.1. However, Auto-Sklearn only supports sklearn up to version 0.21.3. Thanks in advance!