Data mining and business analytics with R:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, N.J.
Wiley
2013
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XI, 351 S. Ill., graph. Darst. |
| ISBN: | 9781118447147 |
Internformat
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Datensatz im Suchindex
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| adam_text | Titel: Data mining and business analytics with R
Autor: Ledolter, Johannes
Jahr: 2013
CONTENTS
Preface ix
Acknowledgments xi
1. Introduction 1
Reference 6
2. Processing the Information and Getting to Know Your Data 7
2.1 Example 1: 2006 Birth Data 7
2.2 Example 2: Alumni Donations 17
2.3 Example 3: Orange Juice 31
References 39
3. Standard Linear Regression 40
3.1 Estimation in R 43
3.2 Example 1: Fuel Efficiency of Automobiles 43
3.3 Example 2: Toyota Used-Car Prices 47
Appendix 3.A The Effects of Model Overfitting on the Average
Mean Square Error of the Regression Prediction 53
References 54
4. Local Polynomial Regression: a Nonparametric Regression
Approach 55
4.1 Model Selection 56
4.2 Application to Density Estimation and the Smoothing
of Histograms 58
4.3 Extension to the Multiple Regression Model 58
4.4 Examples and Software 58
References 65
5. Importance of Parsimony in Statistical Modeling 67
5.1 How Do We Guard Against False Discovery 67
References 70
Vi CONTENTS
6. Penalty-Based Variable Selection in Regression Models with
Many Parameters (LASSO) 71
6.1 Example 1: Prostate Cancer 74
6.2 Example 2: Orange Juice 78
References 82
7. Logistic Regression 83
7.1 Building a Linear Model for Binary Response Data 83
7.2 Interpretation of the Regression Coefficients in a Logistic
Regression Model 85
7.3 Statistical Inference 85
7.4 Classification of New Cases 86
7.5 Estimation in R 87
7.6 Example 1: Death Penalty Data 87
7.7 Example 2: Delayed Airplanes 92
7.8 Example 3: Loan Acceptance 100
7.9 Example 4: German Credit Data 103
References 107
8. Binary Classification, Probabilities, and Evaluating Classification
Performance 108
8.1 Binary Classification 108
8.2 Using Probabilities to Make Decisions 108
8.3 Sensitivity and Specificity 109
8.4 Example: German Credit Data 109
9. Classification Using a Nearest Neighbor Analysis 115
9.1 The fc-Nearest Neighbor Algorithm 116
9.2 Example 1: Forensic Glass 117
9.3 Example 2: German Credit Data 122
Reference 125
10. The Naive Bayesian Analysis: a Model for Predicting
a Categorical Response from Mostly Categorical
Predictor Variables 126
10.1 Example: Delayed Airplanes 127
Reference 131
11. Multinomial Logistic Regression 132
11.1 Computer Software 134
11.2 Example 1: Forensic Glass 134
CONTENTS VÜ
11.3 Example 2: Forensic Glass Revisited 141
Appendix ILA Specification of a Simple Triplet Matrix 147
References 149
12. More on Classification and a Discussion on Discriminant Analysis 150
12.1 Fisher s Linear Discriminant Function 153
12.2 Example 1: German Credit Data 154
12.3 Example 2: Fisher Iris Data 156
12.4 Example 3: Forensic Glass Data 157
12.5 Example 4: MBA Admission Data 159
Reference 160
13. Decision Trees 161
13.1 Example 1: Prostate Cancer 167
13.2 Example 2: Motorcycle Acceleration 179
13.3 Example 3: Fisher Iris Data Revisited 182
14. Further Discussion on Regression and Classification Trees,
Computer Software, and Other Useful Classification Methods 185
14.1 R Packages for Tree Construction 185
14.2 Chi-Square Automatic Interaction Detection (CHAID) 186
14.3 Ensemble Methods: Bagging, Boosting, and Random
Forests 188
14.4 Support Vector Machines (SVM) 192
14.5 Neural Networks 192
14.6 The R Package Rattle: A Useful Graphical User Interface
for Data Mining 193
References 195
15. Clustering 196
15.1 fc-Means Clustering 196
15.2 Another Way to Look at Clustering: Applying the
Expectation-Maximization (EM) Algorithm to Mixtures
of Normal Distributions 204
15.3 Hierarchical Clustering Procedures 212
References 219
16. Market Basket Analysis: Association Rules and Lift 220
16.1 Example 1: Online Radio 222
16.2 Example 2: Predicting Income 227
References 234
VÜi CONTENTS
17. Dimension Reduction: Factor Models and Principal Components 235
17.1 Example 1: European Protein Consumption 238
17.2 Example 2: Monthly US Unemployment Rates 243
18. Reducing the Dimension in Regressions with Multicollinear
Inputs: Principal Components Regression and Partial Least
Squares 247
18.1 Three Examples 249
References 257
19. Text as Data: Text Mining and Sentiment Analysis 258
19.1 Inverse Multinomial Logistic Regression 259
19.2 Example 1: Restaurant Reviews 261
19.3 Example 2: Political Sentiment 266
Appendix 19.A Relationship Between the Gentzkow Shapiro
Estimate of Slant and Partial Least Squares 268
References 271
20. Network Data 272
20.1 Example 1: Marriage and Power in Fifteenth Century
Florence 274
20.2 Example 2: Connections in a Friendship Network 278
References 292
Appendix A: Exercises 293
Exercise 1 294
Exercise 2 294
Exercise 3 296
Exercise 4 298
Exercise 5 299
Exercise 6 300
Exercise 7 301
Appendix B: References 338
Index 341
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