| 書目名稱 | Targeting Uplift | | 副標(biāo)題 | An Introduction to N | | 編輯 | René Michel,Igor Schnakenburg,Tobias von Martens | | 視頻video | http://file.papertrans.cn/901/900644/900644.mp4 | | 概述 | The first book to present uplift modeling in a comprehensive and modern way.Provides the theoretical background on net scoring methods, as well as guidelines for practical applications in real-world p | | 圖書封面 |  | | 描述 | .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.?. | | 出版日期 | Book 2019 | | 關(guān)鍵詞 | net scoring; uplift modelling; incremental response; personalized treatment learning; campaign managemen | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-22625-1 | | isbn_softcover | 978-3-030-22627-5 | | isbn_ebook | 978-3-030-22625-1 | | copyright | Springer Nature Switzerland AG 2019 |
The information of publication is updating
|
|
|Archiver|手機(jī)版|小黑屋|
派博傳思國(guó)際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-6 18:48
發(fā)表于 2025-3-21 19:03:37
