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To my understanding, C4.5 comes with 4 improvements compared to ID3:

  1. Handling missing values in both training data and "test" data,
  2. Handling continuous data
  3. Handling costs on attributes.
  4. The pruning

Source

However, not one of all decision tree python modules that I found, even the so-called C4.5, handles missing values.

Do you know of a library that handles them ? And it would be great if it can work with Pandas DataFrames "out-of-the-box"...

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    $\begingroup$ Have you considered using the H2O package in Python? $\endgroup$
    – grouphug
    Jan 6 '20 at 3:25
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I don't think there is a C4.5 implementation in a popular python library. Your options are :

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  • $\begingroup$ Thanks, I saw some of those GH implementations. Maybe I'm a bit paranoic, but it seems hard to me to use some implementation of an algorithm that it's not very used, as I can't know if it's correct, and I don't think I can read it all. I'll try rpy. $\endgroup$
    – EzrielS
    Jan 25 '20 at 22:47

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