contents

 

front matter

preface

acknowledgments

about this book

about the author

about the cover illustration

Part 1 The basics of ensembles

1 Ensemble methods: Hype or hallelujah?

1.1 Ensemble methods: The wisdom of the crowds

1.2 Why you should care about ensemble learning

1.3 Fit vs. complexity in individual models

Regression with decision trees

Regression with support vector machines

1.4 Our first ensemble

1.5 Terminology and taxonomy for ensemble methods

Part 2 Essential ensemble methods

2 Homogeneous parallel ensembles: Bagging and random forests

2.1 Parallel ensembles

2.2 Bagging: Bootstrap aggregating

Intuition: Resampling and model aggregation