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R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

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Web key resource page judgment using CART

Classification and Regression Trees (CART) is one of the most popular decision tree algorithms. It is a binary recursive partitioning algorithm that can be used to process continuous and nominal attributes.

There are three main steps in the CART algorithm. The first is to construct the maximum tree (binary tree). The second step is to choose the right size of the tree. The last step is to classify new data using the result tree.

Compared to other algorithms, there are many important characteristics of CART:

  • Binary decision tree (a binary recursive partitioning process)
  • The source dataset can have continuous or nominal attributes
  • No stopping rule (unless no possible splits are available)
  • Tree pruning with cost-complexity ...

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