Evaluating learning algorithms: a classification perspective
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2011
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XVI, 406 S. graph. Darst. |
| ISBN: | 9780521196000 |
Internformat
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| 245 | 1 | 0 | |a Evaluating learning algorithms |b a classification perspective |c Nathalie Japkowicz ; Mohak Shah |
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Datensatz im Suchindex
| _version_ | 1804145651153371136 |
|---|---|
| adam_text | Titel: Evaluating learning algorithms
Autor: Japkowicz, Nathalie
Jahr: 2011
Contents
Preface page xi
Acronyms xv
1 Introduction 1
1.1 The De Facto Culture 3
1.2 Motivations for This Book 6
1.3 The De Facto Approach 7
1.4 Broader Issues with Evaluation Approaches 12
1.5 What Can We Do? 16
1.6 Is Evaluation an End in Itself? 18
1.7 Purpose of the Book 19
1.8 Other Takes on Evaluation 20
1.9 Moving Beyond Classification 20
1.10 Thematic Organization 21
2 Machine Learning and Statistics Overview 23
2.1 Machine Learning Overview 23
2.2 Statistics Overview 42
2.3 Summary 72
2.4 Bibliographic Remarks 73
3 Performance Measures I 74
3.1 Overview of the Problem 75
3.2 An Ontology of Performance Measures 81
3.3 Illustrative Example 82
3.4 Performance Metrics with a Multiclass Focus 85
3.5 Performance Metrics with a Single-Class Focus 94
3.6 Illustration of the Confusion-Matrix-Only-Based Metrics
Using WEKA 107
3.7 Summary
3.8 Bibliographic Remarks
Performance Measures II
4.1 Graphical Performance Measures
4.2 Receiver Operating Characteristic (ROC) Analysis
4.3 Other Visual Analysis Methods
4.4 Continuous and Probabilistic Classifiers
4.5 Specialized Metrics
4.6 Illustration of the Ranking and Probabilistic Approaches
Using R, ROCR, and WEKA
4.7 Summary
4.8 Bibliographic Remarks
Contents
108
109
111
112
112
131
137
143
146
159
159
5 Error Estimation 161
5.1 Introduction 163
5.2 Holdout Approach 164
5.3 What Implicitly Guides Resampling? 167
5.4 Simple Resampling 171
5.5 A Note on Model Selection 177
5.6 Multiple Resampling 178
5.7 Discussion 185
5.8 Illustrations Using R 187
5.9 Summary 202
5.10 Bibliographic Remarks 202
Appendix: Proof of Equation (5.5) 204
6 Statistical Significance Testing 206
6.1 The Purpose of Statistical Significance Testing 207
6.2 The Limitations of Statistical Significance Testing 210
6.3 An Overview of Relevant Statistical Tests 213
6.4 A Note on Terminology 215
6.5 Comparing Two Classifiers on a Single Domain 217
6.6 Comparing Two Classifiers on Multiple Domains 231
6.7 Comparing Multiple Classifiers on Multiple Domains 239
6.8 Statistical Tests for Two Classifiers on a Single Domain
Based on Resampling Techniques 258
6.9 Illustration of the Statistical Tests Application Using R 263
6.10 Summary 289
6.11 Bibliographic Remarks 290
7 Datasets and Experimental Framework 292
7.1 Repository-Based Approach 294
7.2 Making Sense of Our Repositories: Metalearning 300
Contents ix
7.3 Artificial Data Approach 301
7.4 Community Participation: Web-Based Solutions 304
7.5 Summary 306
7.6 Bibliographie Remarks 306
8 Recent Developments 308
8.1 Performance Metrics 309
8.2 Frameworks for Performance Metrics 312
8.3 Combining Metrics 317
8.4 Insights from Statistical Learning Theory 323
8.5 Other Developments 329
8.6 Summary 330
Appendix: Proof of Theorems 8.1 and 8.2 330
9 Conclusion 335
9.1 An Evaluation Framework Template 336
9.2 Concluding Remarks 349
9.3 Bibliographic Remarks 350
Appendix A: Statistical Tables 351
A.1 The Z Table 351
A.2 The t Table 352
A.3 The/2 Table 353
A.4 The Table of Critical Values for the Signed Test 355
A.5 The Wilcoxon Table 356
A.6 The F-Ratio Table 357
A.7 The Friedman Table 361
A.8 The Table of Critical Values for the Tukey Test 362
A.9 The Table of Critical Values for the Dunnett Test 363
Appendix B: Additional Information on the Data 364
Appendix C: Two Case Studies 368
C.I Illustrative Case Study 1 368
C.2 Illustrative Case Study 2 375
Bibliography 393
Index 403
|
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| language | English |
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| spelling | Japkowicz, Nathalie Verfasser (DE-588)14398425X aut Evaluating learning algorithms a classification perspective Nathalie Japkowicz ; Mohak Shah 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2011 XVI, 406 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Klassifikation (DE-588)4030958-7 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Algorithmus (DE-588)4001183-5 s Klassifikation (DE-588)4030958-7 s DE-604 Shah, Mohak Sonstige (DE-588)143984322 oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022523681&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Japkowicz, Nathalie Evaluating learning algorithms a classification perspective Maschinelles Lernen (DE-588)4193754-5 gnd Algorithmus (DE-588)4001183-5 gnd Klassifikation (DE-588)4030958-7 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4001183-5 (DE-588)4030958-7 |
| title | Evaluating learning algorithms a classification perspective |
| title_auth | Evaluating learning algorithms a classification perspective |
| title_exact_search | Evaluating learning algorithms a classification perspective |
| title_full | Evaluating learning algorithms a classification perspective Nathalie Japkowicz ; Mohak Shah |
| title_fullStr | Evaluating learning algorithms a classification perspective Nathalie Japkowicz ; Mohak Shah |
| title_full_unstemmed | Evaluating learning algorithms a classification perspective Nathalie Japkowicz ; Mohak Shah |
| title_short | Evaluating learning algorithms |
| title_sort | evaluating learning algorithms a classification perspective |
| title_sub | a classification perspective |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Algorithmus (DE-588)4001183-5 gnd Klassifikation (DE-588)4030958-7 gnd |
| topic_facet | Maschinelles Lernen Algorithmus Klassifikation |
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