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 $zsim1$.
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