Scikit-learn C4.5 tree classifier

Forked from RaczeQ/scikit-learn-C4.5-tree-classifier
Changes made

Added pre-pruning to limit training of the model's depth to a certain depth to avoid overfitting

A C4.5 tree classifier based on the zhangchiyu10/pyC45 repository, refactored to be compatible with the scikit-learn library.

To use this classifier, just copy c45 directory to your project and import classifier where you need it using from c45 import C45 line

Example usage can be found in a main.py file:

>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import train_test_split
>>> from c45 import C45
>>>
>>> iris = load_iris()
>>> clf = C45(attrNames=iris.feature_names)
>>> X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.5)
>>> clf.fit(X_train, y_train)
C45(attrNames=['sepallengthcm', 'sepalwidthcm', 'petallengthcm', 'petalwidthcm'])
>>> print(f'Accuracy: {clf.score(X_test, y_test)}')
Accuracy: 0.8533333333333334
>>> clf.printTree()
<?xml version="1.0" ?>
<DecisionTree>
        <petallengthcm flag="l" p="0.413" value="3.7">0</petallengthcm>
        <petallengthcm flag="r" p="0.587" value="3.7">
                <petallengthcm flag="l" p="0.591" value="4.8">1</petallengthcm>
                <petallengthcm flag="r" p="0.409" value="4.8">
                        <petallengthcm flag="l" p="0.111" value="5.0">
                                <sepallengthcm flag="l" p="0.5" value="6.3">2</sepallengthcm>
                                <sepallengthcm flag="r" p="0.5" value="6.3">1</sepallengthcm>
                        </petallengthcm>
                        <petallengthcm flag="r" p="0.889" value="5.0">2</petallengthcm>
                </petallengthcm>
        </petallengthcm>
</DecisionTree>
>>> # Method to stop growing the tree after a certain depth
>>> depth = 5 
>>> clf.fit_fixed_depth(X_train, y_train, depth)