Research surveys tend to fall on either end of the spectrum: either they are so high level and cursory in their treatment that they are useful only as a dictionary of terms in the field, or they are so deep and terse that the discussion can only be followed by those already experienced in the field. Ensemble Methods in Data Mining (Seni and Elder, 2010) strikes a good balance between these extremes. This book is an accessible introduction to the theory and practice of ensemble methods in machine learning, with sufficient detail for a novice to begin experimenting right away, and copious references for researchers interested in further details of algorithms and proofs. The treatment focuses on the use of decision trees as base learners (as they are the most common choice), but the principles discussed are applicable with any modeling algorithm. The authors also provide a nice discussion of cross-validation and of the more common regularization techniques.
The heart of the text is the chapter on the Importance Sampling. The authors frame the classic ensemble methods (bagging, boosting, and random forests) as special cases of the Importance Sampling methodology. This not only clarifies the explanations of each approach, but also provides a principled basis for finding improvements to the original algorithms. They have one of the clearest explanations of AdaBoost that I’ve ever read.
A major shortcoming of ensemble methods is the loss of interpretability, when compared to single-model methods such as Decision Trees or Linear Regression. The penultimate chapter is on “Rule Ensembles”: an attempt at a more interpretable ensemble learner. They also discuss measures for variable importance and interaction strength. The last chapter discusses Generalized Degrees of Freedom as an alternative complexity measure and its relationship to potential over-fit.
Overall, I found the book clear and concise, with good attention to practical details. I appreciated the snippets of R code and the references to relevant R packages. One minor nitpick: this book has also been published digitally, presumably with color figures. Because the print version is grayscale, some of the color-coded graphs are now illegible. Usually the major points of the figure are clear from the context in the text; still, the color to grayscale conversion is something for future authors in this series to keep in mind.
Data scientist with Win Vector LLC. I also dance, read ghost stories and folklore, and sometimes blog about it all.