Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions (Synthesis Lectures on Data Mining and Knowledge Discovery)
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From the Inside Flap
"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."
About the Author
- Publisher : Morgan and Claypool Publishers (February 24, 2010)
- Language : English
- Paperback : 126 pages
- ISBN-10 : 1608452840
- ISBN-13 : 978-1608452842
- Item Weight : 8.2 ounces
- Dimensions : 7.5 x 0.29 x 9.25 inches
- Best Sellers Rank: #1,861,302 in Books (See Top 100 in Books)
- Customer Reviews:
Top reviews from the United States
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But overall, this is a must-read book if you are in the data science field.
On one side, the book seems rather light for an academic audience (it only surfaces each topic). On the other side, it is too academic for industry practitioners. So it’s not fully clear who the target audience is.
To be noted issues regarding missing axis label on some pictures. Also the quality of certain pictures is really low. In conclusion, I would recommend it only if you need an overview of techniques in the field and are not scared of reading equations instead of plain English.
PS. Morgan Claypool sell the book's PDF for $20, or $0 for those affiliated with the publisher's institutional subscribers.
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.
Top reviews from other countries
If on the other hand you want a short introduction this may or may not work depending on your current knowledge of the area.
The book tries to highlight many areas and a definite shortfall is the lack of depth provided on each subject area covered.
The price is also a steep one for such a short title and as offered by another reviewer the eBook format available free online is likely a better bet especially for students.
At just over 90 pages of useful information, this book will be a quick read and depending on the readers level of expertise a quick intro or a succinct overview of the methods available in this evolving area of machine learning.
Better value with comparable coverage of the subject area is available for the practitioner in the Handbook of Statistical Analysis & Data Mining Applications also authored by one of the writers of this executive summary of ensemble methods.