  We estimate the evolution of the galaxy-galaxy merger fraction for
$M_\star>10^{10.5}M_\odot$ galaxies over $0.25<z<1$ in the $\sim$18.6 deg$^2$
deep CLAUDS+HSC-SSP surveys. We do this by training a Random Forest Classifier
to identify merger candidates from a host of parametric morphological features,
and then visually follow-up likely merger candidates to reach a high-purity,
high-completeness merger sample. Correcting for redshift-dependent detection
bias, we find that the merger fraction at $z=0$ is 1.0$\pm$0.2%, that the
merger fraction evolves as $(1+z)^{2.3 \pm 0.4}$, and that a typical massive
galaxy has undergone $\sim$0.3 major mergers since $z=1$. This pilot study
illustrates the power of very deep ground-based imaging surveys combined with
machine learning to detect and study mergers through the presence of faint, low
surface brightness merger features out to at least $z\sim1$.
