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Classification and Regression Trees (Wadsworth Statistics/Probability) Paperback

ISBN-13: 978-0412048418 ISBN-10: 0412048418 Edition: 1st

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Classification and Regression Trees (Wadsworth Statistics/Probability) + C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning) + The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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Product Details

  • Series: Wadsworth Statistics/Probability
  • Paperback: 368 pages
  • Publisher: Chapman and Hall/CRC; 1 edition (January 1, 1984)
  • Language: English
  • ISBN-10: 0412048418
  • ISBN-13: 978-0412048418
  • Product Dimensions: 9.4 x 6 x 0.8 inches
  • Shipping Weight: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.8 out of 5 stars  See all reviews (6 customer reviews)
  • Amazon Best Sellers Rank: #244,140 in Books (See Top 100 in Books)

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36 of 36 people found the following review helpful By Michael R. Chernick on January 24, 2008
Format: 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.
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21 of 22 people found the following review helpful By T.S. Lim on May 5, 2000
Format: 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.
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19 of 20 people found the following review helpful By Alan Mead (amead@soltec.net) on December 22, 1998
Format: 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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