  Data visualization should be accessible for all analysts with data, not just
the few with technical expertise. Visualization recommender systems aim to
lower the barrier to exploring basic visualizations by automatically generating
results for analysts to search and select, rather than manually specify. Here,
we demonstrate a novel machine learning-based approach to visualization
recommendation that learns visualization design choices from a large corpus of
datasets and associated visualizations. First, we identify five key design
choices made by analysts while creating visualizations, such as selecting a
visualization type and choosing to encode a column along the X- or Y-axis. We
train models to predict these design choices using one million
dataset-visualization pairs collected from a popular online visualization
platform. Neural networks predict these design choices with high accuracy
compared to baseline models. We report and interpret feature importances from
one of these baseline models. To evaluate the generalizability and uncertainty
of our approach, we benchmark with a crowdsourced test set, and show that the
performance of our model is comparable to human performance when predicting
consensus visualization type, and exceeds that of other ML-based systems.
