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＜電子ブック＞
Targeting Uplift : An Introduction to Net Scores / by René Michel, Igor Schnakenburg, Tobias von Martens

版	1st ed. 2019.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2019
本文言語	英語
大きさ	XXXII, 352 p. 144 illus., 119 illus. in color : online resource
著者標目	*Michel, René author Schnakenburg, Igor author von Martens, Tobias author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Business information services LCSH:Data mining LCSH:Marketing FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Business Information Systems FREE:Data Mining and Knowledge Discovery FREE:Marketing FREE:Statistical Theory and Methods
一般注記	List of Symbols -- List of Figures -- List of Tables -- Introduction -- The Traditional Approach: Gross Scoring -- Basic Net Scoring Methods: The Uplift Approach -- Validation of Net Models: Measuring Stability and Discriminatory Power -- Supplementary Methods for Variable Transformation and Selection -- A Simulation Framework for the Validation of Research Hypotheses on Net Scoring -- Software Implementations -- Data Prerequisites -- Practical Issues and Business Cases -- Summary and Outlook -- Appendix -- Other Literature on Net Scoring -- Index.- This book explores all relevant aspects of net scoring, also known as uplift modeling: a data mining approach used to analyze and predict the effects of a given treatment on a desired target variable for an individual observation. After discussing modern net score modeling methods, data preparation, and the assessment of uplift models, the book investigates software implementations and real-world scenarios. Focusing on the application of theoretical results and on practical issues of uplift modeling, it also includes a dedicated chapter on software solutions in SAS, R, Spectrum Miner, and KNIME, which compares the respective tools. This book also presents the applications of net scoring in various contexts, e.g. medical treatment, with a special emphasis on direct marketing and corresponding business cases. The target audience primarily includes data scientists, especially researchers and practitioners in predictive modeling and scoring, mainly, but not exclusively, in the marketingcontext. HTTP:URL=https://doi.org/10.1007/978-3-030-22625-1

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