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Targeting Uplift : an Introduction to Net Scores

Author: René Michel; Igor Schnakenburg; Tobias Von Martens
Publisher: Cham : Springer, 2019.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Michel, René.
Targeting Uplift : An Introduction to Net Scores.
Cham : Springer, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: René Michel; Igor Schnakenburg; Tobias Von Martens
ISBN: 9783030226251 3030226255
OCLC Number: 1119635379
Notes: 7.1 SAS Base/SAS Foundation or SAS Enterprise Guide
Description: 1 online resource (373 pages)
Contents: Intro; Preface; Reference; Contents; List of Symbols and Abbreviations; List of Figures; List of Tables; 1 Introduction; 1.1 Problem Statement; 1.2 State-of-the-Art; 1.3 Structure of the Book; References; 2 The Traditional Approach: Gross Scoring; 2.1 Problem Statement; 2.2 Methods; 2.2.1 Decision Trees; 2.2.2 Regression; 2.2.3 Neural Networks; 2.2.4 Nearest Neighbor; 2.2.5 Bayesian Classifiers; 2.3 Assessment; 2.3.1 Criteria of Model Quality; 2.3.2 Misclassification and Profit/Loss; 2.3.3 Response and Captured (Response); 2.3.4 Gain and Lift; 2.3.5 Receiver Operating Characteristic 2.3.6 Gini Coefficient2.3.7 Average Squared Error; 2.3.8 Model Stability; References; 3 Basic Net Scoring Methods: The Uplift Approach; 3.1 Problem Statement; 3.2 Methods; 3.2.1 Two Independent Models; 3.2.2 Two Dependent Models; 3.2.2.1 The Lo Model; 3.2.2.2 The Tian (Modified Covariate) Method; 3.2.2.3 The Imai-Ratkovic Model; 3.2.3 Equal Target and Control Group Sizes; 3.2.3.1 Uplift Increaser Modeling; 3.2.3.2 Modeling Based on a Probability Decomposition; 3.2.4 One Common Model; 3.2.4.1 Decision Trees; 3.2.4.2 Net Nearest Neighbor (NNN); 3.2.5 Bayesian Classifiers 3.2.5.1 Net Naive Bayesian Classifiers3.2.5.2 Generalized Net Naive Bayesian Classifiers; 3.3 Semi-continuous and Continuous Reaction Targets; 3.3.1 Possible Modifications of Model Assumptions; 3.3.2 A Method for the Semi-continuous Case; 3.3.3 Methods for the Pure Continuous Case; 3.4 More Than Binary Treatment Variables; 3.5 Summary of Net Scoring Methods; References; 4 Validation of Net Models: Measuring Stability and Discriminatory Power; 4.1 Notation for Model Validation; 4.2 Model Stability; 4.2.1 Average Squared Deviation; 4.2.2 A Significance-Based Measure 5.2.1.4 Uplift Impact Correlation5.2.1.5 Rank Uplift Impact Correlation; 5.2.1.6 Net Information Value; 5.2.2 Enhancement of Preselection by Cross Validation; 5.2.3 Comparison of the Preselection Methods on a Real-World Dataset; References; 6 A Simulation Framework for the Validation of Research Hypotheses on Net Scoring; 6.1 Multi-Step Approach to the Generation of Simulated Data; 6.2 Gathering of Historical Real-World Data; 6.3 Production of the Hypothetical Data; 6.4 Dataset Selection for Simulation Studies; References; 7 Software Implementations
Responsibility: René Michel, Igor Schnakenburg, Tobias von Martens.

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  Read more...

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