  The classification of variable objects provides insight into a wide variety
of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky
Transient Facility (ZTF) provides time series observations that record the
variability of more than a billion sources. The scale of these data
necessitates automated approaches to make a thorough analysis. Building on
previous work, this paper reports the results of the ZTF Source Classification
Project (SCoPe), which trains neural network and XGBoost machine learning (ML)
algorithms to perform dichotomous classification of variable ZTF sources using
a manually constructed training set containing 170,632 light curves. We find
that several classifiers achieve high precision and recall scores, suggesting
the reliability of their predictions for 209,991,147 light curves across 77 ZTF
fields. We also identify the most important features for XGB classification and
compare the performance of the two ML algorithms, finding a pattern of higher
precision among XGB classifiers. The resulting classification catalog is
available to the public, and the software developed for SCoPe is open-source
and adaptable to future time-domain surveys.
