This is a C4.5 classifier compatible with scikit-learn
, and more precisely, with scikit-learn.model_selection.GridSearchCV
.
This repo is forked from RaczeQ/scikit-learn-C4.5-tree-classifier, which is in turn based on zhangchiyu10/pyC45.
It is important to pass the feature names to the constructor. In case you use a column transformer, you will need to know the column names beforehand by executing the transformer before the grid search pipeline.
Example usage can be found in a main.py file:
from imblearn.pipeline import Pipeline
from sklearn.compose import make_column_transformer
from sklearn.decomposition import PCA # for example
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier # to have another model to check
from c45 import C45
categorical_preprocessing = make_column_transformer(
...,
remainder='passthrough',
verbose_feature_names_out=False
)
X_tr = categorical_preprocessing.fit_transform(X)
feature_names = categorical_preprocessing.get_feature_names_out()
pipe = Pipeline([
('dimensionality_reduction', 'passthrough'),
('clf', 'passthrough')
])
param_grid = {
'dimensionality_reduction': [
'passthrough',
PCA()
],
'clf': [
*[DecisionTreeClassifier(
random_state=19,
criterion=prd['criterion'],
#max_features=prd['max_features'],
max_depth=prd['max_depth']
) for prd in product_dict(
#max_features=[None, 'sqrt', 'log2'],
max_depth=[4,5,6,7,8,9,10,None],
criterion=['gini', 'entropy', 'log_loss'],
)],
C45(attrNames_=feature_names[:-1])
]
}
cv = GridSearchCV(pipe, param_grid=param_grid, scoring='accuracy', cv=5, n_jobs=-1, verbose=10)
cv.fit(X_tr, y)