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FFTrees : A toolbox to create, visualize, and evaluate fast-and-frugal decision trees

FFTrees : A toolbox to create, visualize, and evaluate fast-and-frugal decision trees

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PHILLIPS, Nathaniel D., Hansjörg NETH, Jan K. WOIKE, Wolfgang GAISSMAIER, 2017. FFTrees : A toolbox to create, visualize, and evaluate fast-and-frugal decision trees. In: Judgment and Decision Making. 12(4), pp. 344-368. eISSN 1930-2975

@article{Phillips2017FFTre-39832, title={FFTrees : A toolbox to create, visualize, and evaluate fast-and-frugal decision trees}, year={2017}, number={4}, volume={12}, journal={Judgment and Decision Making}, pages={344--368}, author={Phillips, Nathaniel D. and Neth, Hansjörg and Woike, Jan K. and Gaissmaier, Wolfgang} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/39832"> <dc:contributor>Phillips, Nathaniel D.</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-08-10T11:54:22Z</dcterms:available> <dcterms:rights rdf:resource="http://nbn-resolving.de/urn:nbn:de:bsz:352-20150914100631302-4485392-8"/> <dc:language>eng</dc:language> <dc:contributor>Gaissmaier, Wolfgang</dc:contributor> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/39832"/> <dc:contributor>Neth, Hansjörg</dc:contributor> <dc:creator>Gaissmaier, Wolfgang</dc:creator> <dcterms:title>FFTrees : A toolbox to create, visualize, and evaluate fast-and-frugal decision trees</dcterms:title> <dc:creator>Neth, Hansjörg</dc:creator> <dc:contributor>Woike, Jan K.</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-08-10T11:54:22Z</dc:date> <dcterms:issued>2017</dcterms:issued> <dcterms:abstract xml:lang="eng">Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees. In this paper, we explain how FFTs work, introduce a new class of algorithms called fan for constructing FFTs, and provide a tutorial for using the FFTrees package. We then conduct a simulation across ten real-world datasets to test how well FFTs created by FFTrees can predict data. Simulation results show that FFTs created by FFTrees can predict data as well as popular classification algorithms such as regression and random forests, while remaining simple enough for anyone to understand and use.</dcterms:abstract> <dc:creator>Phillips, Nathaniel D.</dc:creator> <dc:creator>Woike, Jan K.</dc:creator> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 10.08.2017 (Informationen über die Zugriffsstatistik)

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