CART Algorithm

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A CART algorithm is a decision tree training algorithm that uses a Gini impurity index as a decision tree splitting criterion.





  • (Steinberg, 2009) ⇒ Dan Steinberg. (2009). “CART: Classification and Regression Trees.” In: (Wu & Kumar, 2009), "The Top Ten Algorithms in Data Mining.” Chapman & Hall. ISBN 1420089641
    • QUOTE: The 1984 monograph, “CART: Classification and Regression Trees,” coauthored by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone (BFOS), represents a major milestone in the evolution of artificial intelligence, machine learning, nonparametric statistics, and data mining. The work is important for the comprehensiveness of its study of decision trees, the technical innovations it introduces, its sophisticated examples of tree-structured data analysis, and its authoritative treatment of large sample theory for trees. Since its publication the CART monograph has been cited some 3000 times according to the science and social science citation indexes; Google Scholar reports about 8,450 citations. CART citations can be found in almost any domain, with many appearing in fields such as credit risk, targeted marketing, financial markets modeling, electrical engineering, quality control, biology, chemistry, and clinical medical research. CART has also strongly influenced image compression ...



  • (Breiman et al., 1984) ⇒ Leo Breiman, Jerome H. Friedman, Charles J. Stone, and R. A. Olshen. (1984). “Classification and Regression Trees." Chapman & Hall. ISBN:0412048418
    • Book Overview: 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.