  With a rapidly rising number of transients detected in astronomy,
classification methods based on machine learning are increasingly being
employed. Their goals are typically to obtain a definitive classification of
transients, and for good performance they usually require the presence of a
large set of observations. However, well-designed, targeted models can reach
their classification goals with fewer computing resources. This paper presents
SNGuess, a model designed to find young extragalactic nearby transients with
high purity. SNGuess works with a set of features that can be efficiently
calculated from astronomical alert data. Some of these features are static and
associated with the alert metadata, while others must be calculated from the
photometric observations contained in the alert. Most of the features are
simple enough to be obtained or to be calculated already at the early stages in
the lifetime of a transient after its detection. We calculate these features
for a set of labeled public alert data obtained over a time span of 15 months
from the Zwicky Transient Facility (ZTF). The core model of SNGuess consists of
an ensemble of decision trees, which are trained via gradient boosting.
Approximately 88% of the candidates suggested by SNGuess from a set of alerts
from ZTF spanning from April 2020 to August 2021 were found to be true relevant
supernovae (SNe). For alerts with bright detections, this number ranges between
92% and 98%. Since April 2020, transients identified by SNGuess as potential
young SNe in the ZTF alert stream are being published to the Transient Name
Server (TNS) under the AMPEL_ZTF_NEW group identifier. SNGuess scores for any
transient observed by ZTF can be accessed via a web service. The source code of
SNGuess is publicly available.
