The list of parameters of the modified data set for machine learning (n = 162).

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Autor principal:	Uğur Ejder (22683228) (author)
Altres autors:	Pınar Uskaner Hepsağ (22683231) (author)
Publicat:	2025
Matèries:	Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors
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author	Uğur Ejder (22683228)
author2	Pınar Uskaner Hepsağ (22683231)
author2_role	author
author_facet	Uğur Ejder (22683228) Pınar Uskaner Hepsağ (22683231)
author_role	author
dc.creator.none.fl_str_mv	Uğur Ejder (22683228) Pınar Uskaner Hepsağ (22683231)
dc.date.none.fl_str_mv	2025-11-25T18:24:06Z
dc.identifier.none.fl_str_mv	10.1371/journal.pone.0336846.t002
dc.relation.none.fl_str_mv	https://figshare.com/articles/dataset/The_list_of_parameters_of_the_modified_data_set_for_machine_learning_i_n_i_162_/30713283
dc.rights.none.fl_str_mv	CC BY 4.0 info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv	Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors
dc.title.none.fl_str_mv	The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
dc.type.none.fl_str_mv	Dataset info:eu-repo/semantics/publishedVersion dataset
description	<p>The list of parameters of the modified data set for machine learning (<i>n</i> = 162).</p>
eu_rights_str_mv	openAccess
id	Manara_b4b6eaec7ca3867f456924521ee76588
identifier_str_mv	10.1371/journal.pone.0336846.t002
network_acronym_str	Manara
network_name_str	ManaraRepo
oai_identifier_str	oai:figshare.com:article/30713283
publishDate	2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv	CC BY 4.0
spelling	The list of parameters of the modified data set for machine learning (<i>n</i> = 162).Uğur Ejder (22683228)Pınar Uskaner Hepsağ (22683231)BiotechnologyEcologyCancerBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsmall sample sizelr &# 8211evaluated using 5assisted reproductive technologiesartificial bee colonyart ), yetaddress class imbalancesupport vector machinepharmaceutical supplement useenhance predictive performanceabc hybrids outperformedabc hybrid counterpartslocal interpretable modelabc hybrid modelmodel performancesupplement variablesproducing interpretabledietician supportvitro fertilizationsynthetic minoritystudies relysampling techniqueretrospective datasetregression treerandom forestobserved improvementsnearest neighborslimited optimizationinfluential featuresindividual predictionsimproving predictionimplemented alongsidefuture studiesfour algorithmsfolic acidfold crossexploratory ratherdietary dataconventional algorithmsconcept studyclinically directivebinary representationbaseline modelsalgorithm modelsagnostic explanationsaccuracy ).21 predictors<p>The list of parameters of the modified data set for machine learning (<i>n</i> = 162).</p>2025-11-25T18:24:06ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0336846.t002https://figshare.com/articles/dataset/The_list_of_parameters_of_the_modified_data_set_for_machine_learning_i_n_i_162_/30713283CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307132832025-11-25T18:24:06Z
spellingShingle	The list of parameters of the modified data set for machine learning (<i>n</i> = 162). Uğur Ejder (22683228) Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors
status_str	publishedVersion
title	The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
title_full	The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
title_fullStr	The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
title_full_unstemmed	The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
title_short	The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
title_sort	The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
topic	Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors

The list of parameters of the modified data set for machine learning (<i>n</i> = 162).

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