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Applied Predictive Modeling

Overview of attention for book
Cover of 'Applied Predictive Modeling'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction
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    Chapter 2 A Short Tour of the Predictive Modeling Process
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    Chapter 3 Data Pre-processing
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    Chapter 4 Over-Fitting and Model Tuning
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    Chapter 5 Measuring Performance in Regression Models
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    Chapter 6 Linear Regression and Its Cousins
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    Chapter 7 Nonlinear Regression Models
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    Chapter 8 Regression Trees and Rule-Based Models
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    Chapter 9 A Summary of Solubility Models
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    Chapter 10 Case Study: Compressive Strength of Concrete Mixtures
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    Chapter 11 Measuring Performance in Classification Models
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    Chapter 12 Discriminant Analysis and Other Linear Classification Models
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    Chapter 13 Nonlinear Classification Models
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    Chapter 14 Classification Trees and Rule-Based Models
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    Chapter 15 A Summary of Grant Application Models
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    Chapter 16 Remedies for Severe Class Imbalance
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    Chapter 17 Case Study: Job Scheduling
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    Chapter 18 Measuring Predictor Importance
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    Chapter 19 An Introduction to Feature Selection
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    Chapter 20 Factors That Can Affect Model Performance
Attention for Chapter 16: Remedies for Severe Class Imbalance
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Chapter title
Remedies for Severe Class Imbalance
Chapter number 16
Book title
Applied Predictive Modeling
Published in
Applied Predictive Modeling, January 2013
DOI 10.1007/978-1-4614-6849-3_16
Book ISBNs
978-1-4614-6848-6, 978-1-4614-6849-3
Authors

Max Kuhn, Kjell Johnson

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 27%
Researcher 6 18%
Student > Master 6 18%
Student > Bachelor 2 6%
Student > Doctoral Student 2 6%
Other 4 12%
Unknown 4 12%
Readers by discipline Count As %
Environmental Science 4 12%
Engineering 4 12%
Computer Science 4 12%
Agricultural and Biological Sciences 3 9%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 9 27%
Unknown 7 21%

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