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The C4.5 algorithm uses information gain ratio instead of information gain like ID3, and it also adds pruning.

What does C5.0 add more? Is there any example of code? I looked on the web but there is not anything interesting.

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According to wikipedia:

C5.0 offers a number of improvements on C4.5. Some of these are:[6][7]

  • Speed - C5.0 is significantly faster than C4.5 (several orders of magnitude)
  • Memory usage - C5.0 is more memory efficient than C4.5
  • Smaller decision trees - C5.0 gets similar results to C4.5 with considerably smaller decision trees.
  • Support for boosting - Boosting improves the trees and gives them more accuracy.
  • Weighting - C5.0 allows you to weight different cases and misclassification types.
  • Winnowing - a C5.0 option automatically winnows the attributes to remove those that may be unhelpful.

You can download the source code of a single-threaded version from the official website; it is implemented in C. There is also an unofficial R implementation in the C50 package in CRAN. You can also invoke it from Python via rpy2 following these steps.

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