Ich habe dieses Buch als Basislektüre meiner Diplomarbeit über Entscheidungsbaum verfahren verwendet. Das Buch gibt einen vollständigen und klar abgegrenzten Überblick über alle Arten, Eigenschaften und Funktionsweisen von Entscheidungsbaumverfahren. Insofern hilft es, um sich schnell in die Materie einzuarbeiten, andererseits gehen die Folgekapitel tiefer auf die Materie ein. Alles in allem ein sehr gutes Buch!
5つ星のうち3.0Specialists should consider it, Practitioners should look elsewhere
2008年5月4日にアメリカ合衆国でレビュー済み
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I will recommend this to one or two colleagues, but it will not be something I will recommend to clients.
The first thing you notice about this book is its very academic style. It has numbered paragraphs like 2.0, and 7.3.1.12. It been used a graduate text, presumably for mathematicians and computer scientists. I think it would be good for that purpose. It could work quite well for statisticians that are interested in the details of data mining algorithms. It is in a series in Machine Perception and Artificial Intelligence. Other titles include "Fundamentals of Robotics", and "Bridging the Gap Between Graph Edit Distance and Kernel Machines", so don't confuse this book with something like Data Mining Techniques, which is written for a general audience. It opens the 2nd chapter with (condensed): "A training set is a bag instance of a bag schema. A bag instance is a collection of tuples that may contain duplicates." The folks that I work with can instantly divide themselves into those that would consider a book like this, and those that wouldn't. It cites references in almost every sentence, which can be distracting to the casual reader, and eventually convinced me that I need to read the original authors like Breiman.
Classification and Regression Trees
So having issued a warning, there is plenty to like. The authors have made a real attempt to cover everything - I found 1/3 that I knew, 1/3 that will be quite useful to me, and 1/3 that is too much detail for me. Chapter 3 "Evaluation of Classification Trees" will be great for statisticians that wondered how to judge the efficacy of a tree that was built without hypothesis testing. Also, I was very pleased to see a chapter on "Decision Forests", which is a discussion of "ensemble methods" - in other words combining a set of tree models.
I was hoping for something that would have a detailed chapter on each of the most common decision trees algorithms with briefer sections on the obscure ones. It has all this information, but in a way that I have to work pretty hard to get to it. If you want a quick overview of data mining (even if you think that trees are the method you are going to use), try Data Mining Techniques.
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
If you want to know the details, but are content to learn the details only on the well known techniques (like CHAID and CART) then Larose is a good choice.
Discovering Knowledge in Data: An Introduction to Data Mining