Applied Predictive Modeling 1st edition by Max Kuhn, Kjell Johnson – Ebook PDF Instant Download/Delivery: 1461468493 , 9781461468493
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ISBN 10: 1461468493
ISBN 13: 9781461468493
Author: Max Kuhn, Kjell Johnson
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance―all of which are problems that occur frequently in practice.
The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book’s companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.
This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package.
Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge.
Applied Predictive Modeling 1st Table of contents:
1 Introduction
1.1 Prediction Versus Interpretation
1.2 Key Ingredients of Predictive Models
1.3 Terminology
1.4 Example Data Sets and Typical Data Scenarios
1.5 Overview
1.6 Notation
Part I General Strategies
2 A Short Tour of the Predictive Modeling Process
2.1 Case Study: Predicting Fuel Economy
2.2 Themes
2.3 Summary
3 Data Pre-processing
3.1 Case Study: Cell Segmentation in High-Content Screening
3.2 Data Transformations for Individual Predictors
3.3 Data Transformations for Multiple Predictors
3.4 Dealing with Missing Values
3.5 Removing Predictors
3.6 Adding Predictors
3.7 Binning Predictors
3.8 Computing
Exercises
4 Over-Fitting and Model Tuning
4.1 The Problem of Over-Fitting
4.2 Model Tuning
4.3 Data Splitting
4.4 Resampling Techniques
4.5 Case Study: Credit Scoring
4.6 Choosing Final Tuning Parameters
4.7 Data Splitting Recommendations
4.8 Choosing Between Models
4.9 Computing
Exercises
Part II Regression Models
5 Measuring Performance in Regression Models
5.1 Quantitative Measures of Performance
5.2 The Variance-Bias Trade-off
5.3 Computing
6 Linear Regression and Its Cousins
6.1 Case Study: Quantitative Structure-Activity Relationship Modeling
6.2 Linear Regression
6.3 Partial Least Squares
6.4 Penalized Models
6.5 Computing
Exercises
7 Nonlinear Regression Models
7.1 Neural Networks
7.2 Multivariate Adaptive Regression Splines
7.3 Support Vector Machines
7.4 K-Nearest Neighbors
7.5 Computing
Exercises
8 Regression Trees and Rule-Based Models
8.1 Basic Regression Trees
8.2 Regression Model Trees
8.3 Rule-Based Models
8.4 Bagged Trees
8.5 Random Forests
8.6 Boosting
8.7 Cubist
8.8 Computing
Exercises
9 A Summary of Solubility Models
10 Case Study: Compressive Strength of ConcreteMixtures
10.1 Model Building Strategy
10.2 Model Performance
10.3 Optimizing Compressive Strength
10.4 Computing
Part III Classification Models
11 Measuring Performance in Classification Models
11.1 Class Predictions
11.2 Evaluating Predicted Classes
11.3 Evaluating Class Probabilities
11.4 Computing
12 Discriminant Analysis and Other Linear Classification Models
12.1 Case Study: Predicting Successful Grant Applications
12.2 Logistic Regression
12.3 Linear Discriminant Analysis
12.4 Partial Least Squares Discriminant Analysis
12.5 Penalized Models
12.6 Nearest Shrunken Centroids
12.7 Computing
Exercises
13 Nonlinear Classification Models
13.1 Nonlinear Discriminant Analysis
13.2 Neural Networks
13.3 Flexible Discriminant Analysis
13.4 Support Vector Machines
13.5 K-Nearest Neighbors
13.6 Naïve Bayes
13.7 Computing
Exercises
14 Classification Trees and Rule-Based Models
14.1 Basic Classification Trees
14.2 Rule-Based Models
14.3 Bagged Trees
14.4 Random Forests
14.5 Boosting
14.6 C5.0
14.7 Comparing Two Encodings of Categorical Predictors
14.8 Computing
Exercises
15 A Summary of Grant Application Models
16 Remedies for Severe Class Imbalance
16.1 Case Study: Predicting Caravan Policy Ownership
16.2 The Effect of Class Imbalance
16.3 Model Tuning
16.4 Alternate Cutoffs
16.5 Adjusting Prior Probabilities
16.6 Unequal Case Weights
16.7 Sampling Methods
16.8 Cost-Sensitive Training
16.9 Computing
Exercises
17 Case Study: Job Scheduling
17.1 Data Splitting and Model Strategy
17.2 Results
17.3 Computing
Part IV Other Considerations
18 Measuring Predictor Importance
18.1 Numeric Outcomes
18.2 Categorical Outcomes
18.3 Other Approaches
18.4 Computing
Exercises
19 An Introduction to Feature Selection
19.1 Consequences of Using Non-informative Predictors
19.2 Approaches for Reducing the Number of Predictors
19.3 Wrapper Methods
19.4 Filter Methods
19.5 Selection Bias
19.6 Case Study: Predicting Cognitive Impairment
19.7 Computing
Exercises
20 Factors That Can Affect Model Performance
20.1 Type III Errors
20.2 Measurement Error in the Outcome
20.3 Measurement Error in the Predictors
20.4 Discretizing Continuous Outcomes
20.5 When Should You Trust Your Model’s Prediction?
20.6 The Impact of a Large Sample
20.7 Computing
Exercises
Appendix
A A Summary of Various Models
B An Introduction to R
B.1 Start-Up and Getting Help
B.2 Packages
B.3 Creating Objects
B.4 Data Types and Basic Structures
B.5 Working with Rectangular Data Sets
B.6 Objects and Classes
B.7 R Functions
B.8 The Three Faces of darkblue=
B.9 The AppliedPredictiveModeling Package
B.10 The caret Package
B.11 Software Used in this Text
C Interesting Web Sites
References
Indicies
Computing
General
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