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Learn rule lists from data for classification, regression or subgroup discovery

Project description

MDL Rule Lists for prediction and data mining

This repository contains the code for using rule lists for univariate or multivariate classification or regression and its equivalents in Data Mining and Subgroup Discovery. These models use the Minimum Description Length (MDL) principle as optimality criteria.


This project was written for Python 3.7. All required packages from PyPI are specified in the requirements.txt.

NOTE: This list of packages includes the gmpy2 package.

Example of usage:

import pandas as pd
from rulelist import RuleList
from sklearn import datasets
from sklearn.model_selection import train_test_split

task = 'prediction'
target_model = 'categorical'

data = datasets.load_breast_cancer()
Y = pd.Series(
X = pd.DataFrame(

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3)

model = RuleList(task = task, target_model = target_model), y_train)

y_pred = model.predict(X_test)
from sklearn.metrics import accuracy_score



If there are any questions or issues, please contact me by mail at or open an issue here on Github.


In a machine learning (prediction) context for problems of classification, regression, multi-label classification, multi-category classification, or multivariate regression cite the corresponding bibtex of the first classification application of MDL rule lists:

  title={Interpretable multiclass classification by MDL-based rule lists},
  author={Proen{\c{c}}a, Hugo M and van Leeuwen, Matthijs},
  journal={Information Sciences},

in the context of data mining and subgroup discovery please refer to subgroup lists:

  title={Discovering outstanding subgroup lists for numeric targets using MDL},
  author={Proen{\c{c}}a, Hugo M and Gr{\"u}nwald, Peter and B{\"a}ck, Thomas and van Leeuwen, Matthijs},
  journal={arXiv preprint arXiv:2006.09186},


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