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