  In practice, machine learning (ML) workflows require various different steps,
from data preprocessing, missing value imputation, model selection, to model
tuning as well as model evaluation. Many of these steps rely on human ML
experts. AutoML - the field of automating these ML pipelines - tries to help
practitioners to apply ML off-the-shelf without any expert knowledge. Most
modern AutoML systems like auto-sklearn, H20-AutoML or TPOT aim for high
predictive performance, thereby generating ensembles that consist almost
exclusively of black-box models. This, in turn, makes the interpretation for
the layperson more intricate and adds another layer of opacity for users. We
propose an AutoML system that constructs an interpretable additive model that
can be fitted using a highly scalable componentwise boosting algorithm. Our
system provides tools for easy model interpretation such as visualizing partial
effects and pairwise interactions, allows for a straightforward calculation of
feature importance, and gives insights into the required model complexity to
fit the given task. We introduce the general framework and outline its
implementation autocompboost. To demonstrate the frameworks efficacy, we
compare autocompboost to other existing systems based on the OpenML
AutoML-Benchmark. Despite its restriction to an interpretable model space, our
system is competitive in terms of predictive performance on most data sets
while being more user-friendly and transparent.
