Last edited by Zolom
Monday, May 4, 2020 | History

7 edition of Classification and regression trees found in the catalog.

Classification and regression trees

  • 67 Want to read
  • 22 Currently reading

Published by Chapman & Hall in New York, N.Y .
Written in English

    Subjects:
  • Discriminant analysis,
  • Regression analysis,
  • Trees (Graph theory)

  • Edition Notes

    Includes bibliographical references (p. 342-346) and indexes.

    StatementLeo Breiman ... [et al.].
    SeriesWadsworth statistics/probability series
    The Physical Object
    Paginationx, 358 p. :
    Number of Pages358
    ID Numbers
    Open LibraryOL13633511M
    ISBN 100412048418
    OCLC/WorldCa31403268


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Classification and regression trees by Leo Breiman Download PDF EPUB FB2

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. Classification and Regression Trees reflects these two sides, covering the use Classification and regression trees book trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental.

The NOOK Book (eBook) of the Classification and Regression Trees by Leo Breiman at Barnes & Noble. FREE Shipping on $35 or more. Due to COVID, orders may be : Leo Breiman. Classification and Regression Trees by Leo Breiman,available at Book Depository with free delivery worldwide/5(11).

Classification and regression trees | Leo Breiman, Jerome Friedman, Richard A. Olshen, Charles J. Stone | download | B–OK. Download books for free. Find books. CART Bagging Trees Random Forests Breiman, L., J.

Friedman, R. Olshen, and C. Stone, Classification and regression trees. Wadsworth Books, File Size: 3MB. Examples of the common classification algorithms include logistic regression, Naïve Bayes, decision trees, and K Nearest Neighbors. Here is an example of a classification problem that.

In the second stage we used the decision tree methodology for predicting target variables, based on the classification and regression trees (CART) algorithm by Breiman et al.

() and using a.