swMATH ID: 28404
Software Authors: Rudin, Cynthia; Ertekin, Şeyda
Description: Learning customized and optimized lists of rules with mathematical programming. We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear user-defined tradeoff between training accuracy and sparsity. The flexible framework of mathematical programming allows users to create customized models with a provable guarantee of optimality. The software reviewed as part of this submission was given the DOI (Digital Object Identifier) url{doi:10.5281/zenodo.1344142}.
Homepage: https://github.com/SeydaErtekin/ORL
Keywords: mixed-integer programming; decision trees; decision lists; sparsity; interpretable modeling; associative classification
Related Software: UCI-ml; C4.5; CMAR; AdaBoost.MH; shap; WEKA; PMLB; PySAT; GitHub; XGBoost; Glucose; IMLI; MLIC; MurTree; Scikit; PRIE; C50; BartPy; BayesTree; Orange
Referenced in: 8 Publications

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