Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using machine learning to understand physics graduate school admissions   Among all of the first-year graduate students enrolled in doctoral-granting
physics departments, the percentage of female and racial minority students has
remained unchanged for the past 20 years. The current graduate program
admissions process can create challenges for achieving diversity goals in
physics. In this paper, we will investigate how the various aspects of a
prospective student's application to a physics doctoral program affect the
likelihood the applicant will be admitted. Admissions data was collected from a
large, Midwestern public research university that has a decentralized
admissions process and included applicants' undergraduate GPAs and
institutions, research interests, and GRE scores. Because the collected data
varied in scale, we used supervised machine learning algorithms to create
models that predict who was admitted into the PhD program. We find that using
only the applicant's undergraduate GPA and physics GRE score, we are able to
predict with 75% accuracy who will be admitted to the program.

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The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an