The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
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Most Helpful Customer Reviews
33 of 33 people found the following review helpful:
5.0 out of 5 stars
the book that made tree classification algorithms work better through the concept of pruning,
By
This review is from: Classification and Regression Trees (Paperback)
In 1984 Brieman, Olshen, Friedman and Stone published this book and produced a software product called CART that made tree classification popular. These algorithms were very useful in medical applications and the book illustrated some simple success stories particularly ones from Richard Olshen's experience working in the Medical School at UC San Diego.
Olshen and Gordon did some of the work on the asymptotic theory of recursive partitioning that made the methodology credible to the statistical research community. The methods began to be applied to pattern recognition problems and also to the development of expert systems. Today data miners use these tools. These ideas goes back a lot further than these authors. However, previous attempts at recursive partitioning algorithms tended to grow trees with too many terminal nodes. These authors introduced two important ideas. One was to grow the trees overly long and then prune them back. The second was to continually use cross-validation to evaluate the trees. This book is still very valuable 24 years after it was first published. It is also readible by general audiences for the most part. It now stands as a classic text on the subject of classification and regression trees. There are also books that followed in its footsteps and other places where tree structure comes into play.
20 of 21 people found the following review helpful:
5.0 out of 5 stars
A must have for all serious decision trees researchers,
This review is from: Classification and Regression Trees (Paperback)
This book is a must-have for all serious decision trees researchers. It explains the underlying algorithms of classification and regression trees methods in details. It's not for beginners though. It's a bit outdated by now as trees methodology has advanced much with the invention of boosting, bagging, and arcing.
19 of 20 people found the following review helpful:
4.0 out of 5 stars
tough sledding but worth it,
By Alan Mead (amead@soltec.net) (Lockport, IL) - See all my reviews
This review is from: Classification and Regression Trees (Paperback)
A good introduction to classification and regression trees with a variety of examples. You need never regress again! Many will find some of the technical topics difficult but then I found the statistical grounding to be rewarding in the end. My only complaint is that the book is near worthless to practitioners like myself without software which is a little hard to find and then pricey.
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Inside This Book
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First Sentence:
At the University of California, San Diego Medical Center, when a heart attack patient is admitted, 19 variables are measured during the first 24 hours. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
digit recognition data, variable misclassification costs, optimally pruned subtree, class probability estimation, test sample estimates, digit recognition problem, optimal pruning algorithm, exploratory trees, overall misclassification cost, tree impurity, resubstitution estimate, right sized tree, linear combination split, tree growing procedure, probability estimation problem, tree structured approach, tree structured regression, unit cost case, expected misclassification cost, tree structured classifiers, tree structured classification, node impurity, pruned subtrees, surrogate split, independent test sample Key Phrases - Capitalized Phrases (CAPs): (learn more)
Data Missing, San Diego, University of California, Controls Class, Los Angeles New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
At the University of California, San Diego Medical Center, when a heart attack patient is admitted, 19 variables are measured during the first 24 hours. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
digit recognition data, variable misclassification costs, optimally pruned subtree, class probability estimation, test sample estimates, digit recognition problem, optimal pruning algorithm, exploratory trees, overall misclassification cost, tree impurity, resubstitution estimate, right sized tree, linear combination split, tree growing procedure, probability estimation problem, tree structured approach, tree structured regression, unit cost case, expected misclassification cost, tree structured classifiers, tree structured classification, node impurity, pruned subtrees, surrogate split, independent test sample Key Phrases - Capitalized Phrases (CAPs): (learn more)
Data Missing, San Diego, University of California, Controls Class, Los Angeles New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
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- Principles of Data Mining and Knowledge Discovery: First European Symposium, PKDD '97, Trondheim, Norway, June 24-27, 1997 Proceedings (Lecture Notes ... / Lecture Notes in Artificial Intelligence) by Jan Komorowski on 4 pages
- Machine Learning: ECML 2004: 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings (Lecture Notes in Computer ... / Lecture Notes in Artificial Intelligence) by Jean-Francois Boulicaut on 4 pages
- Advances in Intelligent Data Analysis. Reasoning about Data: Second International Symposium, IDA-97, London, UK, August 4-6, 1997, Proceedings (Lecture Notes in Computer Science) by Xiaohui Liu on page 198, page 222, and page 245
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