  The WHT Enhanced Area Velocity Explorer (WEAVE) is a new, massively
multiplexing spectrograph. This new instrument will be exploited to obtain high
S/N spectra of $\sim$25000 galaxies at intermediate redshifts for the WEAVE
Stellar Population Survey (WEAVE-StePS). We test machine learning methods for
retrieving the key physical parameters of galaxies from WEAVE-StePS-like
spectra using both photometric and spectroscopic information at various S/Ns
and redshifts. We simulated $\sim$105000 galaxy spectra assuming SFH with an
exponentially declining star formation rate, covering a wide range of ages,
stellar metallicities, sSFRs, and dust extinctions. We then evaluated the
ability of the random forest and KNN algorithms to correctly predict such
parameters assuming no measurement errors. We checked how much the predictive
ability deteriorates for different S/Ns and redshifts, finding that both
algorithms still accurately estimate the ages and metallicities with low bias.
The dispersion varies from 0.08-0.16 dex for ages and 0.11-0.25 dex for
metallicity, depending on the redshift and S/N. For dust attenuation, we find a
similarly low bias and dispersion. For the sSFR, we find a very good
constraining power for star-forming galaxies, log sSFR$\gtrsim$ -11, where the
bias is $\sim$ 0.01 dex and the dispersion is $\sim$ 0.10 dex. For more
quiescent galaxies, with log sSFR$\lesssim$ -11, we find a higher bias,
0.61-0.86 dex, and a higher dispersion, $\sim$ 0.4 dex, for different S/Ns and
redshifts. Generally, we find that the RF outperforms the KNN. Finally, the
retrieved sSFR was used to successfully classify galaxies as part of the blue
cloud, green valley, or red sequence. We demonstrate that machine learning
algorithms can accurately estimate the physical parameters of simulated
galaxies even at relatively low S/N=10 per angstrom spectra with available
ancillary photometric information.
