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The ability to compare between individuals or organisations fairly isimportant for the development of robust and meaningful quantitative benchmarks.To make fair comparisons, contextual factors must be taken into account, andcomparisons should only be made between similar organisations such as peergroups. Previous benchmarking methods have used linear regression to adjust forcontextual factors, however linear regression is known to be sub-optimal whennonlinear relationships exist between the comparative measure and covariates.In this paper we propose a random forest model for benchmarking that can adjustfor these potential nonlinear relationships, and validate the approach in acase-study of high noise data. We provide new visualisations and numericalsummaries of the fitted models and comparative measures to facilitateinterpretation by both analysts and non-technical audiences. Comparisons can bemade across the cohort or within peer groups, and bootstrapping provides ameans of estimating uncertainty in both adjusted measures and rankings. Weconclude that random forest models can facilitate fair comparisons betweenorganisations for quantitative measures including in cases on complexcontextual factor relationships, and that the models and outputs are readilyinterpreted by stakeholders.

A statistical machine learning approach for benchmarking in the presence of complex contextual factors and peer groups