Predicting soil textural classes using random forest models : learning from imbalanced dataset

dc.contributor.author	Scholten, Thomas	de_DE
dc.contributor.author	Taghizadeh-Mehrjardi, Ruhollah	de_DE
dc.date.accessioned	2022-12-02T10:27:52Z
dc.date.available	2022-12-02T10:27:52Z
dc.date.issued	2022	de_DE
dc.identifier.uri	http://hdl.handle.net/10900/133630
dc.language.iso	en
dc.relation.uri	https://doi.org/10.3390/agronomy12112613	de_DE
dc.title	Predicting soil textural classes using random forest models : learning from imbalanced dataset	de_DE
dc.type	Article	de_DE
utue.personen.pnd	Scholten, Thomas/187344825	de_DE
utue.publikation.seiten	1-16	de_DE
utue.personen.roh	Mallah, Sina	de_DE
utue.personen.roh	Delsouz Khaki, Bahareh	de_DE
utue.personen.roh	Davatgar, Naser	de_DE
utue.personen.roh	Scholten, Thomas	de_DE
utue.personen.roh	Amirian-Chakan, Alireza	de_DE
utue.personen.roh	Emadi, Mostafa	de_DE
utue.personen.roh	Kerry, Ruth	de_DE
utue.personen.roh	Hosein Mosavi, Amir	de_DE
utue.personen.roh	Taghizadeh-Mehrjardi, Ruhollah	de_DE
dcterms.isPartOf.ZSTitelID	Agronomy - Basel : MDPI,	de_DE
dcterms.isPartOf.ZS-Issue	11, Artikel-ID 2613	de_DE
dcterms.isPartOf.ZS-Volume	volume:12	de_DE
utue.publikation.seitengesamt	1 Online-Ressource	de_DE
utue.titel.verfasserangabe	Sina Mallah, Bahareh Delsouz Khaki, Naser Davatgar, Thomas Scholten, Alireza Amirian-Chakan, Mostafa Emadi, Ruth Kerry, Amir Hosein Mosavi and Ruhollah Taghizadeh-Mehrjardi	de_DE
utue.publikation.abrufzeichen	t017	de_DE
utue.publikation.swbdatum	2211	de_DE
utue.artikel.ppn	1823903827	de_DE