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UID:submissions.supercomputing.org_SC23_sess289_spostg107@linklings.com
SUMMARY:Better Data Splits for Machine Learning with Astartes
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nJackson Burns (Mas
 sachusetts Institute of Technology (MIT))\n\nMachine Learning (ML) has bec
 ome an increasingly popular tool to accelerate traditional workflows. Crit
 ical to the use of ML is the process of splitting datasets into training, 
 validation, and testing subsets to develop and evaluate models. Common pra
 ctice is to assign these subsets randomly. Although this approach is fast,
  it only measures a model's capacity to interpolate. These testing errors 
 may be overly optimistic on out-of-scope data; thus, there is a growing ne
 ed to easily measure performance for extrapolation tasks. To address this 
 issue, we report astartes, an open-source Python package that implements m
 any similarity- and distance-based algorithms to partition data into more 
 challenging splits. This poster focuses on use-cases within cheminformatic
 s. However, astartes operates on arbitrary vectors, so its principals and 
 workflow are generalizable to other ML domains as well. astartes is availa
 ble via the Python package managers pip and conda and is publicly hosted o
 n GitHub (github.com/JacksonBurns/astartes).\n\nRegistration Category: Tec
 h Program Reg Pass, Exhibits Reg Pass
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