Predicting wine quality from terrain characteristics with regression trees

Predicting wine quality from terrain characteristics with regression trees

A former cartographic study on terrain characteristics of the German Rhinegau is reviewed. An attempt is made to predict relative quality of the Riesling from local site factors usings Classifcation And Regression Trees (= CART). Valid results suppose quantity to be ruled out by quality, ignoring an...

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Autor principal: Reiner Schwarz
Formato: article
Lenguaje:DE
EN
FR
IT
PT
Publicado: Unité Mixte de Recherche 8504 Géographie-cités 1997
Materias:
wine quality
terrain characteristic
regression classification
statistics
agriculture
vineyard ecology
Geography (General)
G1-922
Acceso en línea:https://doaj.org/article/b5b3d31014e546a09fd5faa50881dec8
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spelling oai:doaj.org-article:b5b3d31014e546a09fd5faa50881dec82021-12-02T11:18:10ZPredicting wine quality from terrain characteristics with regression trees1278-336610.4000/cybergeo.361https://doaj.org/article/b5b3d31014e546a09fd5faa50881dec81997-11-01T00:00:00Zhttp://journals.openedition.org/cybergeo/361https://doaj.org/toc/1278-3366A former cartographic study on terrain characteristics of the German Rhinegau is reviewed. An attempt is made to predict relative quality of the Riesling from local site factors usings Classifcation And Regression Trees (= CART). Valid results suppose quantity to be ruled out by quality, ignoring any price ratio of vine cultivation. The study demonstrates that CART is a valuable statistical tool without restrictions by data types.Reiner SchwarzUnité Mixte de Recherche 8504 Géographie-citésarticlewine qualityterrain characteristicregression classificationstatisticsagriculturevineyard ecologyGeography (General)G1-922DEENFRITPTCybergeo (1997)
institution DOAJ
collection DOAJ
language DE
EN
FR
IT
PT
topic wine quality
terrain characteristic
regression classification
statistics
agriculture
vineyard ecology
Geography (General)
G1-922
spellingShingle wine quality
terrain characteristic
regression classification
statistics
agriculture
vineyard ecology
Geography (General)
G1-922
Reiner Schwarz
Predicting wine quality from terrain characteristics with regression trees
description A former cartographic study on terrain characteristics of the German Rhinegau is reviewed. An attempt is made to predict relative quality of the Riesling from local site factors usings Classifcation And Regression Trees (= CART). Valid results suppose quantity to be ruled out by quality, ignoring any price ratio of vine cultivation. The study demonstrates that CART is a valuable statistical tool without restrictions by data types.
format article
author Reiner Schwarz
author_facet Reiner Schwarz
author_sort Reiner Schwarz
title Predicting wine quality from terrain characteristics with regression trees
title_short Predicting wine quality from terrain characteristics with regression trees
title_full Predicting wine quality from terrain characteristics with regression trees
title_fullStr Predicting wine quality from terrain characteristics with regression trees
title_full_unstemmed Predicting wine quality from terrain characteristics with regression trees
title_sort predicting wine quality from terrain characteristics with regression trees
publisher Unité Mixte de Recherche 8504 Géographie-cités
publishDate 1997
url https://doaj.org/article/b5b3d31014e546a09fd5faa50881dec8
work_keys_str_mv AT reinerschwarz predictingwinequalityfromterraincharacteristicswithregressiontrees
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