Applied predictive modeling:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Springer
[2013]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke. |
| Beschreibung: | xiii, 600 Seiten Illustrationen, Diagramme (überwiegend farbig) |
| ISBN: | 9781461468486 |
Internformat
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| 245 | 1 | 0 | |a Applied predictive modeling |c Max Kuhn, Kjell Johnson |
| 264 | 1 | |a New York |b Springer |c [2013] | |
| 264 | 4 | |c © 2013 | |
| 300 | |a xiii, 600 Seiten |b Illustrationen, Diagramme (überwiegend farbig) | ||
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Datensatz im Suchindex
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| DE-BY-FWS_katkey | 614557 |
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| _version_ | 1808594879896354816 |
| adam_text |
Contents
1 Introduction. 1
1.1 Prediction Versus Interpretation. . 4
1.2 Key Ingredients of Predictive Models . 5
1.3 Terminology. 6
1.4 Example Data Sets and Typical Data Scenarios. 7
1.5 Overview. 14
1.6 Notation. 15
Part I General Strategies
2 A Short Tour of the Predictive Modeling Process. 19
2.1 Case Study: Predicting Fuel Economy . 19
2.2 Themes. 24
2.3 Summary. 26
3 Data Pre-processing. 27
3.1 Case Study: Cell Segmentation in High-Content Screening . 28
3.2 Data Transformations for Individual Predictors. 30
3.3 Data Transformations for Multiple Predictors. 33
3.4 Dealing with Missing Values. 41
3.5 Removing Predictors. 43
3.6 Adding Predictors . 47
3.7 Binning Predictors. 49
3.8 Computing. 51
Exercises . 58 4
4 Over-Fitting and Model Tuning. 61
4.1 The Problem of Over-Fitting. 62
4.2 Model Tuning. 64
4.3 Data Splitting . 67
4.4 Resampling Techniques. 69
ix
_ Contents
A
4.5 Case Study: Credit Scoring. 73
4.6 Choosing Final Tuning Parameters. 74
4.7 Data Splitting Recommendations. 77
4.8 Choosing Between Models. · · 78
4.9 Computing. 80
Exercises . 89
Part II Regression Models
5 Measuring Performance in Regression Models. 95
5.1 Quantitative Measures of Performance. 95
5.2 The Variance-Bias Trade-off. 97
5.3 Computing. 98
6 Linear Regression and Its Cousins.101
6.1 Case Study: Quantitative Structure-Activity Relationship
Modeling. 102
6.2 Linear Regression. . 105
6.3 Partial Least Squares.112
6.4 Penalized Models.122
6.5 Computing.128
Exercises.137
7 Nonlinear Regression Models.141
7.1 Neural Networks.141
7.2 Multivariate Adaptive Regression Splines.145
7.3 Support Vector Machines.151
7.4 if-Nearest Neighbors .159
7.5 Computing.161
Exercises .168
8 Regression Trees and Rule-Based Models.173
8.1 Basic Regression Trees.175
8.2 Regression Model Trees.184
8.3 Rule-Based Models.190
8.4 Bagged Trees. 192
8.5 Random Forests.198
8.6 Boosting.
8.7 Cubist.
8.8 Computing. ։ ։ 212
Exercises. 21g
Contents xi
9 A Summary of Solubility Models.221
10 Case Study: Compressive Strength of Concrete
Mixtures.225
10.1 Model Building Strategy.229
10.2 Model Performance.230
10.3 Optimizing Compressive Strength.233
10.4 Computing.236
Part III Classification Models
11 Measuring Performance in Classification Models.247
11.1 Class Predictions. 247
11.2 Evaluating Predicted Classes.254
11.3 Evaluating Class Probabilities.262
11.4 Computing.266
12 Discriminant Analysis and Other Linear Classification
Models.275
12.1 Case Study: Predicting Successful Grant Applications.275
12.2 Logistic Regression.282
12.3 Linear Discriminant Analysis .287
12.4 Partial Least Squares Discriminant Analysis.297
12.5 Penalized Models.302
12.6 Nearest Shrunken Centroids.306
12.7 Computing.308
Exercises .326
13 Nonlinear Classification Models.329
13.1 Nonlinear Discriminant Analysis.329
13.2 Neural Networks.333
13.3 Flexible Discriminant Analysis.338
13.4 Support Vector Machines.343
13.5 A-Nearest Neighbors .350
13.6 Naive Bayes.353
13.7 Computing.358
Exercises .366
14 Classification Trees and Rule-Based Models.369
14.1 Basic Classification Trees.370
14.2 Rule-Based Models.383
14.3 Bagged Trees.385
14.4 Random Forests.386
14.5 Boosting.389
14.6 C5.0.392
xii Contents
14.7 Comparing Two Encodings of Categorical Predictors.400
14.8 Computing.400
Exercises .411
15 A Summary of Grant Application Models .415
16 Remedies for Severe Class Imbalance. 419
16.1 Case Study: Predicting Caravan Policy Ownership.419
16.2 The Effect of Class Imbalance.420
16.3 Model Tuning.423
16.4 Alternate Cutoffs.423
16.5 Adjusting Prior Probabilities.426
16.6 Unequal Case Weights.426
16.7 Sampling Methods. 427
16.8 Cost-Sensitive Training .429
16.9 Computing.435
Exercises .442
17 Case Study: Job Scheduling .445
17.1 Data Splitting and Model Strategy.450
17.2 Results.454
17.3 Computing.457
Part IV Other Considerations
18 Measuring Predictor Importance.463
18.1 Numeric Outcomes. 464
18.2 Categorical Outcomes.468
18.3 Other Approaches. 472
18.4 Computing .478
Exercises .484
19 An Introduction to Feature Selection.487
19.1 Consequences of Using Non-informative Predictors.488
19.2 Approaches for Reducing the Number of Predictors.490
19.3 Wrapper Methods. 491
19.4 Filter Methods.499
19.5 Selection Bias.500
19.6 Case Study: Predicting Cognitive Impairment .502
19.7 Computing. 511
Exercises .518
Contents xiii
20 Factors That Can Affect Model Performance.521
20.1 Type III Errors.522
20.2 Measurement Error in the Outcome.524
20.3 Measurement Error in the Predictors.527
20.4 Discretizing Continuous Outcomes.531
20.5 When Should You Trust Your Model’s Prediction?.534
20.6 The Impact of a Large Sample.538
20.7 Computing.541
Exercises .542
Appendix
A A Summary of Various Models .549
B An Introduction to R .551
B.l Start-Up and Getting Help.551
B.2 Packages.552
B.3 Creating Objects.553
B.4 Data Types and Basic Structures.554
B.5 Working with Rectangular Data Sets.558
B.6 Objects and Classes.560
B.7 R Functions.561
B.8 The Three Faces of =. . . . .562
B.9 The AppliedPredictiveModeting Package.562
B.10 The caret Package.563
B.ll Software Used in this Text.565
C Interesting Web Sites.567
References.569
Indicies
Computing.591
General.595
Max Kuhn · Kjell Johnson
Applied Predictive Modeling
This text is intended for a broad audience as both an introduction to predictive models
as well as a guide to applying them. Non ֊mathematical readers will appreciate the
intuitive explanations of the techniques while an emphasis on problem-solving with real
data across a wide variety of applications will aid practitioners who wish to extend their
expertise. Readers should have knowledge of basic statistical ideas, such as correlation
and linear regression analysis. While the text is biased against complex equations, a
mathematical background is needed for advanced topics.
Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R D in Groton
Connecticut. He has been applying predictive models in the pharmaceutical and
diagnostic industries for over 15 years and is the author of a number of R packages.
Dr. Johnson has more than a decade of statistical consulting and predictive modeling
experience in pharmaceutical research and development. He is a co-founder of Arbor
Analytics, a firm specializing in predictive modeling and is a former Director of
Statistics at Pfizer Global R D. His scholarly work centers on the application and
development of statistical methodology and learning algorithms. |
| any_adam_object | 1 |
| author | Kuhn, Max Johnson, Kjell |
| author_GND | (DE-588)1093876328 (DE-588)109387998X |
| author_facet | Kuhn, Max Johnson, Kjell |
| author_role | aut aut |
| author_sort | Kuhn, Max |
| author_variant | m k mk k j kj |
| building | Verbundindex |
| bvnumber | BV041435183 |
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| ctrlnum | (OCoLC)858008583 (DE-599)BVBBV041435183 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik Wirtschaftswissenschaften Medizin |
| format | Book |
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| illustrated | Illustrated |
| indexdate | 2024-08-28T04:01:25Z |
| institution | BVB |
| isbn | 9781461468486 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026882064 |
| oclc_num | 858008583 |
| open_access_boolean | |
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| physical | xiii, 600 Seiten Illustrationen, Diagramme (überwiegend farbig) |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| spellingShingle | Kuhn, Max Johnson, Kjell Applied predictive modeling Vorhersagetheorie (DE-588)4188671-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4188671-9 (DE-588)4358095-6 (DE-588)4114528-8 |
| title | Applied predictive modeling |
| title_auth | Applied predictive modeling |
| title_exact_search | Applied predictive modeling |
| title_full | Applied predictive modeling Max Kuhn, Kjell Johnson |
| title_fullStr | Applied predictive modeling Max Kuhn, Kjell Johnson |
| title_full_unstemmed | Applied predictive modeling Max Kuhn, Kjell Johnson |
| title_short | Applied predictive modeling |
| title_sort | applied predictive modeling |
| topic | Vorhersagetheorie (DE-588)4188671-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | Vorhersagetheorie Prognoseverfahren Mathematisches Modell |
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| work_keys_str_mv | AT kuhnmax appliedpredictivemodeling AT johnsonkjell appliedpredictivemodeling |
Inhaltsverzeichnis
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