Conservation machine learning: a case study of random forests

Abstract Conservation machine learning conserves models across runs, users, and experiments—and puts them to good use. We have previously shown the merit of this idea through a small-scale preliminary experiment, involving a single dataset source, 10 datasets, and a single so-called cultivation meth...

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Main Authors:	Moshe Sipper, Jason H. Moore
Format:	article
Language:	EN
Published:	Nature Portfolio 2021
Subjects:	Medicine R Science Q
Online Access:	https://doaj.org/article/b36e655ed8424d698572cc136b219b51
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spelling	oai:doaj.org-article:b36e655ed8424d698572cc136b219b512021-12-02T12:09:32ZConservation machine learning: a case study of random forests10.1038/s41598-021-83247-42045-2322https://doaj.org/article/b36e655ed8424d698572cc136b219b512021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83247-4https://doaj.org/toc/2045-2322Abstract Conservation machine learning conserves models across runs, users, and experiments—and puts them to good use. We have previously shown the merit of this idea through a small-scale preliminary experiment, involving a single dataset source, 10 datasets, and a single so-called cultivation method—used to produce the final ensemble. In this paper, focusing on classification tasks, we perform extensive experimentation with conservation random forests, involving 5 cultivation methods (including a novel one introduced herein—lexigarden), 6 dataset sources, and 31 datasets. We show that significant improvement can be attained by making use of models we are already in possession of anyway, and envisage the possibility of repositories of models (not merely datasets, solutions, or code), which could be made available to everyone, thus having conservation live up to its name, furthering the cause of data and computational science.Moshe SipperJason H. MooreNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Moshe Sipper Jason H. Moore Conservation machine learning: a case study of random forests
description	Abstract Conservation machine learning conserves models across runs, users, and experiments—and puts them to good use. We have previously shown the merit of this idea through a small-scale preliminary experiment, involving a single dataset source, 10 datasets, and a single so-called cultivation method—used to produce the final ensemble. In this paper, focusing on classification tasks, we perform extensive experimentation with conservation random forests, involving 5 cultivation methods (including a novel one introduced herein—lexigarden), 6 dataset sources, and 31 datasets. We show that significant improvement can be attained by making use of models we are already in possession of anyway, and envisage the possibility of repositories of models (not merely datasets, solutions, or code), which could be made available to everyone, thus having conservation live up to its name, furthering the cause of data and computational science.
format	article
author	Moshe Sipper Jason H. Moore
author_facet	Moshe Sipper Jason H. Moore
author_sort	Moshe Sipper
title	Conservation machine learning: a case study of random forests
title_short	Conservation machine learning: a case study of random forests
title_full	Conservation machine learning: a case study of random forests
title_fullStr	Conservation machine learning: a case study of random forests
title_full_unstemmed	Conservation machine learning: a case study of random forests
title_sort	conservation machine learning: a case study of random forests
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b36e655ed8424d698572cc136b219b51
work_keys_str_mv	AT moshesipper conservationmachinelearningacasestudyofrandomforests AT jasonhmoore conservationmachinelearningacasestudyofrandomforests
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Conservation machine learning: a case study of random forests

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