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Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions (Synthesis Lectures on Data Mining and Knowledge Discovery) Paperback – February 24, 2010
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"The practical implementations of ensemble methods are enormous. Most current implementations of them are quite primitive and this book will definitely raise the state of the art. Giovanni Seni's thorough mastery of the cutting-edge research and John Elder's practical experience have combined to make an extremely readable and useful book."
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Top Customer Reviews
The heart of the text is the chapter on 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 descriptions of AdaBoost that I've ever read.
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; it is probably of more interest to researchers and mathematicians than to practitioners.
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.
Ensemble methods detailed in this books gave me the 'ah ha'. It gave a nicely balanced flavor of easy implementation and difficult concepts. I really enjoyed the book. I was able to finish the book quick and would save it for reference.
If there is anything that I would want to see in more detail, it is the treatment of evaluation of model prediction. It is a bit light on how to tell if the final product is really working. Given that the book is an intro, then it is not really a mis-treatment.
overall, awesome small book.
PS. Morgan Claypool sell the book's PDF for $20, or $0 for those affiliated with the publisher's institutional subscribers.
Most Recent Customer Reviews
Excellent overview of ensemble methods. The chapter about Importance Sampling provides nice methodology for generalization of classical ensemble methods.Published 2 months ago by hkar
Excellent introduction to Ensemble methods. Good for beginners.Published 10 months ago by M. Shannon
The definitive reference on ensembles, which are a central, profound aspect of predictive modeling best practices. Read morePublished on April 13, 2010 by Eric Siegel