Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A statistical machine learning approach for benchmarking in the presence
  of complex contextual factors and peer groups   The ability to compare between individuals or organisations fairly is
important for the development of robust and meaningful quantitative benchmarks.
To make fair comparisons, contextual factors must be taken into account, and
comparisons should only be made between similar organisations such as peer
groups. Previous benchmarking methods have used linear regression to adjust for
contextual factors, however linear regression is known to be sub-optimal when
nonlinear relationships exist between the comparative measure and covariates.
In this paper we propose a random forest model for benchmarking that can adjust
for these potential nonlinear relationships, and validate the approach in a
case-study of high noise data. We provide new visualisations and numerical
summaries of the fitted models and comparative measures to facilitate
interpretation by both analysts and non-technical audiences. Comparisons can be
made across the cohort or within peer groups, and bootstrapping provides a
means of estimating uncertainty in both adjusted measures and rankings. We
conclude that random forest models can facilitate fair comparisons between
organisations for quantitative measures including in cases on complex
contextual factor relationships, and that the models and outputs are readily
interpreted by stakeholders.

3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex