  The third data release of Gaia has provided approximately 220 million low
resolution spectra. Among these, about 100,000 correspond to white dwarfs. The
magnitude of this quantity of data precludes the possibility of performing
spectral analysis and type determination by human inspection. In order to
tackle this issue, we explore the possibility of utilising a machine learning
approach, based on a Random Forest algorithm. We aim to analyze the viability
of the Random Forest algorithm for the spectral classification of the white
dwarf population within 100 pc from the Sun, based on the Hermite coefficients
of Gaia spectra. We utilized the assigned spectral type from the Montreal White
Dwarf Database for training and testing our Random Forest algorithm. Once
validated, our algorithm model is applied to the rest of unclassified white
dwarfs within 100 pc. First, we started by classifying the two major spectral
type groups of white dwarfs: hydrogen-rich (DA) and hydrogen-deficient
(non-DA). Next, we explored the possibility of classifying the various spectral
subtypes, including in some cases the secondary spectral types. Our Random
Forest classification presented a very high recall (>80%) for DA and DB white
dwarfs, and a very high precision (>90%) for DB, DQ and DZ white dwarfs. As a
result we have assigned a spectral type to 9,446 previously unclassified white
dwarfs: 4,739 DAs, 76 DBs (60 of them DBAs), 4,437 DCs, 132 DZs and 62 DQs (9
of them DQpec). Despite the low resolution of Gaia spectra, the Random Forest
algorithm applied to the Gaia spectral coefficients proves to be a highly
valuable tool for spectral classification.
