This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures.
This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures.
James D. Malley is a Research Mathematical Statistician in the Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, at the National Institutes of Health.
Inhaltsangabe
Preface Acknowledgements Part I. Introduction: 1. Prologue 2. The landscape of learning machines 3. A mangle of machines 4. Three examples and several machines Part II. A Machine Toolkit: 5. Logistic regression 6. A single decision tree 7. Random forests - trees everywhere Part III. Analysis Fundamentals: 8. Merely two variables 9. More than two variables 10. Resampling methods 11. Error analysis and model validation Part IV. Machine Strategies: 12. Ensemble methods - let's take a vote 13. Summary and conclusions References Index.
Preface; Acknowledgements; Part I. Introduction: 1. Prologue; 2. The landscape of learning machines; 3. A mangle of machines; 4. Three examples and several machines; Part II. A Machine Toolkit: 5. Logistic regression; 6. A single decision tree; 7. Random forests - trees everywhere; Part III. Analysis Fundamentals: 8. Merely two variables; 9. More than two variables; 10. Resampling methods; 11. Error analysis and model validation; Part IV. Machine Strategies: 12. Ensemble methods - let's take a vote; 13. Summary and conclusions; References; Index.
Preface Acknowledgements Part I. Introduction: 1. Prologue 2. The landscape of learning machines 3. A mangle of machines 4. Three examples and several machines Part II. A Machine Toolkit: 5. Logistic regression 6. A single decision tree 7. Random forests - trees everywhere Part III. Analysis Fundamentals: 8. Merely two variables 9. More than two variables 10. Resampling methods 11. Error analysis and model validation Part IV. Machine Strategies: 12. Ensemble methods - let's take a vote 13. Summary and conclusions References Index.
Preface; Acknowledgements; Part I. Introduction: 1. Prologue; 2. The landscape of learning machines; 3. A mangle of machines; 4. Three examples and several machines; Part II. A Machine Toolkit: 5. Logistic regression; 6. A single decision tree; 7. Random forests - trees everywhere; Part III. Analysis Fundamentals: 8. Merely two variables; 9. More than two variables; 10. Resampling methods; 11. Error analysis and model validation; Part IV. Machine Strategies: 12. Ensemble methods - let's take a vote; 13. Summary and conclusions; References; Index.
Rezensionen
'The book is well written and provides nice graphics and numerous applications.' Michael R. Chernick, Technometrics
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