Is there a real C4.5 implementation in Python ? (handling missing value) To my understanding, C4.5 comes with 4 improvements compared to ID3:


Handling missing values in both training data and "test" data,
Handling continuous data
Handling costs on attributes.
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"...
I don't think there is a C4.5 implementation in a popular python library. Your options are :


Try github implementations such as : https://github.com/geerk/C45algorithm
Try getting R implementations with rpy package : see over there for an exemple of how that would work (C5.0) https://stackoverflow.com/questions/41070087/calling-c5-0-in-python