rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Targeting Uplift
    sub title: An Introduction to Net Scores
  name:
    name part: Michel, René.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Schnakenburg, Igor.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: von Martens, Tobias.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXXII, 352 p. 144 illus., 119 illus. in color. online resource.
  abstract: 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. .
  table of contents: 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.-.
  note: by René Michel, Igor Schnakenburg, Tobias von Martens.
  subject:
    topic: Statistics
  subject:
    topic: Management information systems
  subject:
    topic: Data mining
  subject:
    topic: Marketing
  subject:
    topic: Statistics for Business, Management, Economics, Finance, Insurance
  subject:
    topic: Business Information Systems
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Marketing
  subject:
    topic: Statistical Theory and Methods
  classification: QA276-280
  classification: 330.015195
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783030226251
  identifier: https://doi.org/10.1007/978-3-030-22625-1
  record info:
    record creation date: 190909
    record change date: 20191025112327.0
    record identifier: 978-3-030-22625-1