  Next-generation surveys like the Legacy Survey of Space and Time (LSST) on
the Vera C. Rubin Observatory will generate orders of magnitude more
discoveries of transients and variable stars than previous surveys. To prepare
for this data deluge, we developed the Photometric LSST Astronomical
Time-series Classification Challenge (PLAsTiCC), a competition which aimed to
catalyze the development of robust classifiers under LSST-like conditions of a
non-representative training set for a large photometric test set of imbalanced
classes. Over 1,000 teams participated in PLAsTiCC, which was hosted in the
Kaggle data science competition platform between Sep 28, 2018 and Dec 17, 2018,
ultimately identifying three winners in February 2019. Participants produced
classifiers employing a diverse set of machine learning techniques including
hybrid combinations and ensemble averages of a range of approaches, among them
boosted decision trees, neural networks, and multi-layer perceptrons. The
strong performance of the top three classifiers on Type Ia supernovae and
kilonovae represent a major improvement over the current state-of-the-art
within astronomy. This paper summarizes the most promising methods and
evaluates their results in detail, highlighting future directions both for
classifier development and simulation needs for a next generation PLAsTiCC data
set.
