Extracting optimal actionable plans from additive tree models
Extracting optimal actionable plans from additive tree models
About this item
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Publisher
Beijing: Higher Education Press
Journal title
Language
English
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Publication information
Publisher
Beijing: Higher Education Press
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Scope and Contents
Contents
Although amazing progress has been made in ma- chine learning to achieve high generalization accuracy and ef- ficiency, there is still very limited work on deriving meaning- ful decision-making actions from the resulting models. How- ever, in many applications such as advertisement, recommen- dation systems, social networks, customer relationship m...
Alternative Titles
Full title
Extracting optimal actionable plans from additive tree models
Authors, Artists and Contributors
Author / Creator
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TN_cdi_proquest_journals_2918720128
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2918720128
Other Identifiers
ISSN
2095-2228
E-ISSN
2095-2236
DOI
10.1007/s11704-016-5273-4
