c50py: C5.0 decision trees for Python, with native categorical splits

Decision trees were born able to split on categories. Quinlan's ID3, C4.5 and C5.0 ask questions like "is the sector in {mining, tourism, construction}?". scikit-learn's CART only splits on numbers, so categorical columns have to be one-hot encoded first, and one question becomes a staircase of dummy variables.

c50py brings the C5.0 way back to Python, as a scikit-learn estimator:

Same data, same test accuracy (0.782), both pruned by 5-fold cross-validation. CART needs three dummy variables and 8 leaves; C5.0 asks one question. Reproduce it with examples/make_readme_figure.py.

Install

pip install c50py

Python 3.9+, NumPy and scikit-learn. pandas is optional (DataFrames are used directly) and matplotlib is needed for plot_tree.

Quickstart

import pandas as pd
from sklearn.model_selection import train_test_split
from c50py import C5Classifier

url = "https://raw.githubusercontent.com/daviddiazsolis/c50py/main/examples/titanic.csv"
df = pd.read_csv(url)
X = df[["pclass", "sex", "age", "fare", "embarked"]].astype({"pclass": "category"})
y = df["survived"].map({0: "died", 1: "survived"})
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.3, random_state=0)

# no one-hot encoding, no imputation: sex, embarked and pclass are used as
# categories, and the missing ages are handled by the tree
clf = C5Classifier(min_samples_leaf=10).fit(X_tr, y_tr)
print(f"test accuracy: {clf.score(X_te, y_te):.3f}")      # 0.794

for rule in clf.export_rules():
    print(rule)
# sex IN {male} => died
# sex NOT IN {male} AND pclass IN {3} AND fare <= 23.3500 => survived
# sex NOT IN {male} AND pclass IN {3} AND fare > 23.3500 => died
# sex NOT IN {male} AND pclass NOT IN {3} => survived

clf.plot_tree(filled=True, rounded=True)   # same look and options as sklearn.tree.plot_tree

A scikit-learn estimator

C5Classifier and C5Regressor pass scikit-learn's check_estimator, so they work with clone, Pipeline, cross_val_score, GridSearchCV and the rest of the ecosystem:

from sklearn.model_selection import GridSearchCV

grid = GridSearchCV(C5Classifier(), {"cf": [0.1, 0.25, 0.5], "min_samples_leaf": [1, 5, 20]}, cv=5)
grid.fit(X_tr, y_tr)
print(grid.best_params_, grid.score(X_te, y_te))

What C5.0 does differently from CART

Main features

• Classification and regression: C5Classifier, C5Regressor.
• Categorical columns detected automatically: pandas category, object, string and bool columns, or object columns holding strings. You can also list them with categorical_features=["region", 3] (names or indices).
• Missing values (None, np.nan, pd.NA) in training and prediction.
• Case weights through sample_weight.
• Boosting with trials > 1.
• Readable output: export_rules() (one rule per leaf), predict_rule(X) (the rule each case follows) and print_tree().
• Drawings identical to scikit-learn: plot_tree, export_graphviz and export_text, with the same parameters. Boxes use c50py's own palette so a C5.0 tree is easy to tell apart from a CART tree (palette="sklearn" gives scikit-learn's colours).

Regression

from sklearn.datasets import load_diabetes
from c50py import C5Regressor

X, y = load_diabetes(return_X_y=True, as_frame=True)
reg = C5Regressor(min_samples_leaf=20).fit(X, y)
print(reg.export_rules()[:3])

Key parameters

The Usage Guide covers every option.

How close is it to Quinlan's C5.0?

c50py is a from-scratch Python implementation that follows C4.5/C5.0 in the split criterion, the handling of missing values and the pessimistic pruning. Some parts of the original are not (yet) implemented:

• splits are binary; categorical splits group categories into two subsets (C5.0's default is one branch per category, with subset grouping as an option);
• no winnowing (C5.0's built-in feature selection);
• no rulesets: export_rules() returns the rules of the tree, one per leaf, not C5.0's simplified, independent rules;
• boosting follows AdaBoost.M1/SAMME reweighting, close to but not identical with C5.0's;
• no misclassification costs.

Rulesets and winnowing are on the roadmap. Contributions are welcome.

Citation

If you use c50py in research, please cite it (a JOSS submission is in preparation; meanwhile cite the repository):

@software{diaz_solis_c50py,
  author = {D{\'i}az Sol{\'i}s, David},
  title  = {c50py: C5.0 decision trees for Python},
  url    = {https://github.com/daviddiazsolis/c50py},
  year   = {2026}
}

Contributing and license

Bug reports and pull requests are welcome; see CONTRIBUTING.md and the CHANGELOG. c50py is released under the MIT License. The drawing code is adapted from scikit-learn (BSD-3-Clause).