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Targeting upliftan introduction to n...

Michel, Rene.

Targeting upliftan introduction to net scores /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Targeting upliftby Rene Michel, Igor Schnakenburg, Tobias von Martens.
其他題名:	an introduction to net scores /
作者:	Michel, Rene.
其他作者:	Schnakenburg, Igor.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xxxii, 352 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
電子資源:	https://doi.org/10.1007/978-3-030-22625-1
ISBN:	9783030226251$q(electronic bk.)

Targeting upliftan introduction to net scores /
Michel, Rene.

Targeting upliftan introduction to net scores /[electronic resource] :by Rene Michel, Igor Schnakenburg, Tobias von Martens. - Cham :Springer International Publishing :2019. - xxxii, 352 p. :ill., digital ;24 cm.

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 marketing context.

ISBN: 9783030226251$q(electronic bk.)

Standard No.: 10.1007/978-3-030-22625-1doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / M534 2019

Dewey Class. No.: 006.312

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505 0 $a 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.
520 $a 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 marketing context.
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