國立虎尾科技大學 |

Targeting Uplift = An Introduction to Net Scores /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Targeting Uplift/ by René Michel, Igor Schnakenburg, Tobias von Martens.
其他題名:	An Introduction to Net Scores /
作者:	Michel, René.
其他作者:	Schnakenburg, Igor.
面頁冊數:	XXXII, 352 p. 144 illus., 119 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics . -
電子資源:	https://doi.org/10.1007/978-3-030-22625-1
ISBN:	9783030226251

Targeting Uplift = An Introduction to Net Scores /
Michel, René.

Targeting UpliftAn Introduction to Net Scores /[electronic resource] :by René Michel, Igor Schnakenburg, Tobias von Martens. - 1st ed. 2019. - XXXII, 352 p. 144 illus., 119 illus. in color.online resource.

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

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

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 330.015195

Targeting Uplift = An Introduction to Net Scores /
LDR:02992nam a22004095i 4500 001 1015933
003 DE-He213
005 20200704102141.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030226251 $9 978-3-030-22625-1
024 7 $a 10.1007/978-3-030-22625-1 $2 doi
035 $a 978-3-030-22625-1
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a BUS061000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a K $2 thema
082 0 4 $a 330.015195 $2 23
100 1 $a Michel, René. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1310189
245 1 0 $a Targeting Uplift $h [electronic resource] : $b An Introduction to Net Scores / $c by René Michel, Igor Schnakenburg, Tobias von Martens.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XXXII, 352 p. 144 illus., 119 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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. .
650 0 $a Statistics . $3 1253516
650 0 $a Management information systems. $3 561123
650 0 $a Data mining. $3 528622
650 0 $a Marketing. $3 557931
650 1 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 1211158
650 2 4 $a Business Information Systems. $3 669204
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Statistical Theory and Methods. $3 671396
700 1 $a Schnakenburg, Igor. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1310190
700 1 $a von Martens, Tobias. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1310191
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030226244
776 0 8 $i Printed edition: $z 9783030226268
776 0 8 $i Printed edition: $z 9783030226275
856 4 0 $u https://doi.org/10.1007/978-3-030-22625-1
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)