  We estimate ([M/H], [$\alpha$/M]) for 48 million giants and dwarfs in low
dust extinction regions from the Gaia DR3 XP spectra by using tree-based
machine-learning models trained on APOGEE DR17 and metal-poor star sample of Li
et al. The root mean square error of our estimation is 0.0890 dex for [M/H] and
0.0436 dex for [$\alpha$/M], when we evaluate our models with the test data.
Our estimation is most reliable for giants and metal-rich stars, because the
training data are dominated by such stars. The high-[$\alpha$/M] stars and
low-[$\alpha$/M] stars selected by our ([M/H], [$\alpha$/M]) show different
kinematical properties for giants and low-temperature dwarfs. We further
investigate how our machine-learning models extract information on ([M/H],
[$\alpha$/M]). Intriguingly, our models seem to extract information on
[$\alpha$/M] from Na D lines (589 nm) and Mg I line (516 nm). This result is
understandable given the observed correlation between Na and Mg abundances in
the literature. The catalog of ([M/H], [$\alpha$/M]) as well as their
associated uncertainties will be publicly available online.
