  As spectroscopic surveys continue to grow in size, the problem of classifying
spectra targeted as quasars (QSOs) will need to move beyond its historical
reliance on human experts. Instead, automatic classifiers will increasingly
become the dominant classification method, leaving only small fractions of
spectra to be visually inspected in ambiguous cases. In order to maximise
classification accuracy, making best use of available classifiers will be of
great importance, particularly when looking to identify and eliminate
distinctive failure modes. In this work, we demonstrate that the machine
learning-based classifier QuasarNET will be of use for future surveys such as
the Dark Energy Spectroscopic Instrument (DESI), comparing its performance to
the DESI pipeline classifier redrock. During the first of four passes across
its footprint DESI will need to select high-$z$ ($z\geq2.1$) QSOs for
reobservation, and so we first assess the classifiers' performance at
identifying high-$z$ QSOs from single-exposure spectra. We then quantify the
classifiers' abilities to construct QSO catalogues in both low- and high-$z$
bins, using coadded spectra to simulate end-of-survey data. For such tasks,
QuasarNET is able to outperform redrock in its current form, identifying
approximately 99% of high-$z$ QSOs from single exposures and producing QSO
catalogues with sub-percent levels of contamination. By combining QuasarNET and
redrock's outputs, we can further improve the classification strategies to
identify up to 99.5% of high-$z$ QSOs from single exposures and reduce final
QSO catalogue contamination to below 0.5%. These combined strategies address
DESI's QSO classification needs effectively.
