Random projections and kernelised leave one cluster out cross validation: universal baselines and evaluation tools for supervised machine learning of material properties

Durdy, Samantha ORCID: 0000-0003-3742-2306, Gaultois, Michael W ORCID: 0000-0003-2172-2507, Gusev, Vladimir V ORCID: 0000-0002-2815-607X, Bollegala, Danushka ORCID: 0000-0003-4476-7003 and Rosseinsky, Matthew J ORCID: 0000-0002-1910-2483 (2022) Random projections and kernelised leave one cluster out cross validation: universal baselines and evaluation tools for supervised machine learning of material properties. Digital Discovery, 1 (6). pp. 763-778.

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Official URL: http://dx.doi.org/10.1039/d2dd00039c

Abstract

<jats:p>Kernelised LOCO-CV can measure the extrapolatory power of an algorithm. Random projections are a versatile benchmark for composition featurisation.</jats:p>

Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science Faculty of Science and Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	26 Sep 2023 14:07
Last Modified:	14 Mar 2024 22:24
DOI:	10.1039/d2dd00039c
Open Access URL:	https://doi.org/10.1039/D2DD00039C
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3173076