- Taschenbuch: 126 Seiten
- Verlag: Morgan and Claypool Publishers (24. Februar 2010)
- Sprache: Englisch
- ISBN-10: 1608452840
- ISBN-13: 978-1608452842
- Größe und/oder Gewicht: 19,1 x 23,5 x 0,7 cm
- Durchschnittliche Kundenbewertung: 3.0 von 5 Sternen Alle Rezensionen anzeigen (1 Kundenrezension)
- Amazon Bestseller-Rang: Nr. 387.667 in Englische Bücher (Siehe Top 100 in Englische Bücher)
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Kurzbeschreibung
This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners. 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. - from Foreword 1 by Jaffray Woodriff
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0 von 1 Kunden fanden die folgende Rezension hilfreich
Format:Taschenbuch|Von Amazon bestätigter Kauf
Der schmale Band ist eine gut geschriebene Übersicht über praktisch relevante Ensemble Methoden. Die Autoren gehen nicht auf alle Feinheiten ein, sie präsentieren jedoch jeden Algorithmus mit Pseudo-Kode. Der Text enthält auch zahlreiche Farb-Graphiken. Zumindest liest man im Text von grünen, blauen, roten Punkten bzw. Linien. Nur sieht man davon im Buch nix. Es ist alles Grau in Grau. Wobei noch verschärfend hinzukommt, dass die Grauwerte der verschiedenen Farben praktisch identisch gewählt wurden. Damit sind die Grafiken weitgehend sinnlos. Offensichtlich hat man eine Power-Point Präsentation ohne jede weitere Verarbeitung 1:1 gedruckt. Ein Lektorat gibt es offensichtlich nicht mehr. Einem Lektor hätte auch auffallen müssen, dass es in den References einen Friedman, J. und einen Friedman, J.H. gibt. Nachdem der Name Friedman im Wissenschaftsbetrieb relativ häufig anzutreffen ist, könnte es sich um einen oder zwei verschiedene Autoren handeln. Ich habe mir die Papers heruntergeladen. Es ist ein- und dieselbe Person. Die einheitliche Schreibweise von Autorennamen ist wohl ein Luxus aus längst vergangen Tagen.
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6 Rezensionen
6 von 6 Kunden fanden die folgende Rezension hilfreich
really helpful in learning the method
12. Juni 2010
Format:Taschenbuch
During my 10 plus years of modeling experience, I have always paid most of my attention on variable selection, predictive power, effectiveness and efficiency of a single model form such as logistic model, ordinary regression, tree, etc. From time to time, I also segment my sample space into pieces and then apply different modeling techniques. Never really aware of the concept of 'model selection' or 'model combination'. That classical approach has served me well. But I always suspected that there was a better approach to combine different methods to get better predictions.
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.
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.
4 von 4 Kunden fanden die folgende Rezension hilfreich
Great Need to Know Info on Ensembles
30. Oktober 2010
Format:Taschenbuch|Von Amazon bestätigter Kauf
This is a really great (short) book in my opinion. It contains the best "need to know" information found in the Elements in Statistical Learning, and other good books on data mining. The included R code is a big bonus. I am enjoying reading it so far, and I highly recommend it. The only thing that frustrates me is that the online version on the publishers website is in color, while the print version is not. This is the only reason I did not give it 5 stars. I saw the online version first, and thought that the print version would be in color as well. I am sadly mistaken. There are many graphics in this book that reference different colors and it just looks really crappy in grayscale. If you are familiar with the Elements of Statistical Learnining, imagine printing that out in grayscale and you will know what I mean.
3 von 3 Kunden fanden die folgende Rezension hilfreich
Clear, accessible introduction
1. August 2011
Format:Taschenbuch
This book is an accessible introduction to the theory and practice of ensemble methods in machine learning. It is a quick read, has sufficient detail for a novice to begin experimenting, and copious references for those who are interested in digging deeper. The authors also provide a nice discussion of cross-validation, and their section on regularization techniques is much more straightforward, in my opinion, than the equivalent sections in The Elements of Statistical Learning (Elements is a wonderful, necessary book, but a hard read).
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.
Recommended.
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.
Recommended.
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