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Classification and regression trees: a powerful yet simple technique for the analysis of complex ecological data

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dc.contributor Australian Institute of Marine Science (AIMS) en
dc.contributor.author De'ath, Glenn (AG) en
dc.contributor.author Fabricius, Katharina (KE) en
dc.date.accessioned 2013-02-28T06:46:46Z
dc.date.accessioned 2017-03-21T01:11:15Z
dc.date.accessioned 2019-07-08T01:59:52Z
dc.date.available 2013-02-28T06:46:46Z
dc.date.available 2017-03-21T01:11:15Z
dc.date.available 2019-07-08T01:59:52Z
dc.date.issued 2000-01-01 en
dc.identifier 5812 en
dc.identifier.citation De'ath AG and Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for the analysis of complex ecological data. Ecology. 81: 3178-3192. en
dc.identifier.uri http://epubs.aims.gov.au/11068/5812
dc.description Link to abstract/full text - https://doi.org/10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2
dc.description.abstract Classification and regression trees are ideally suited for the analysis of complex ecological data. For such data, we require flexible and robust analytical methods, which can deal with nonlinear relationships, high-order interactions, and missing values. Despite such difficulties, the methods should be simple to understand and give easily interpretable results. Trees explain variation of a single response variable by repeatedly splitting the data into more homogeneous groups, using combinations of explanatory variables that may be categorical and/or numeric. Each group is characterized by a typical value of the response variable, the number of observations in the group, and the values of the explanatory variables that define it. The tree is represented graphically, and this aids exploration and understanding.
dc.language en en
dc.relation.ispartof Ecology - pages: 81: 3178-3192 en
dc.relation.uri http://data.aims.gov.au/metadataviewer/uuid/2b671780-4415-4c26-8926-7519b0bffce0 en
dc.relation.uri https://apps.aims.gov.au/metadata/view/273fd55d-84d3-4781-a193-ab58695cb4c4
dc.title Classification and regression trees: a powerful yet simple technique for the analysis of complex ecological data en
dc.type journal article en


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