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Searches for gravitational waves produced by coalescing black hole binarieswith total masses $\gtrsim25\,$M$_\odot$ use matched filtering with templatesof short duration. Non-Gaussian noise bursts in gravitational wave detectordata can mimic short signals and limit the sensitivity of these searches.Previous searches have relied on empirically designed statistics incorporatingsignal-to-noise ratio and signal-based vetoes to separate gravitational wavecandidates from noise candidates. We report on sensitivity improvementsachieved using a multivariate candidate ranking statistic derived from asupervised machine learning algorithm. We apply the random forest of baggeddecision trees technique to two separate searches in the high mass $\left(\gtrsim25\,\mathrm{M}_\odot \right)$ parameter space. For a search which issensitive to gravitational waves from the inspiral, merger, and ringdown (IMR)of binary black holes with total mass between $25\,$M$_\odot$ and$100\,$M$_\odot$, we find sensitive volume improvements as high as $70_{\pm13}-109_{\pm 11}$\% when compared to the previously used ranking statistic. Fora ringdown-only search which is sensitive to gravitational waves from theresultant perturbed intermediate mass black hole with mass roughly between$10\,$M$_\odot$ and $600\,$M$_\odot$, we find sensitive volume improvements ashigh as $61_{\pm 4}-241_{\pm 12}$\% when compared to the previously usedranking statistic. We also report how sensitivity improvements can differdepending on mass regime, mass ratio, and available data quality information.Finally, we describe the techniques used to tune and train the random forestclassifier that can be generalized to its use in other searches forgravitational waves.

Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence