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Interpretable decision trees through MaxSAT.

Josep Alòs1, Carlos Ansótegui1, Eduard Torres1

  • 1Logic & Optimization Group (LOG), University of Lleida, Lleida, Spain.

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|January 2, 2023
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Summary
This summary is machine-generated.

This study introduces a new method using Maximum Satisfiability to create Minimum Pure Decision Trees (MPDTs). These MPDTs enhance the accuracy and interpretability of machine learning models, outperforming standard decision trees.

Keywords:
Decision treesExplainable AIMaxSAT

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Decision Trees (DTs) are widely used in machine learning for their interpretability.
  • A key challenge in DT development is balancing predictive accuracy with model interpretability.
  • Existing methods may not achieve optimal performance across both metrics.

Purpose of the Study:

  • To enhance the accuracy-interpretability trade-off in Machine Learning (ML) Decision Trees (DTs).
  • To introduce a novel approach for computing Minimum Pure Decision Trees (MPDTs).
  • To demonstrate the superior performance of MPDTs compared to standard DTs.

Main Methods:

  • Application of Maximum Satisfiability (MaxSAT) technology to DT generation.
  • Development of an algorithm to compute Minimum Pure Decision Trees (MPDTs).
  • Comparison of MPDTs against DTs generated using the scikit-learn (sklearn) ML framework.

Main Results:

  • The proposed MaxSAT-based approach successfully computes MPDTs.
  • The runtime of the MPDT computation is improved compared to previous methods.
  • MPDTs demonstrate superior accuracy compared to DTs generated by sklearn.
  • The interpretability of the generated MPDTs is maintained or improved.

Conclusions:

  • Maximum Satisfiability is an effective technique for generating accurate and interpretable Decision Trees.
  • The developed MPDT approach offers a significant improvement over existing ML decision tree algorithms.
  • This work provides a pathway for more reliable and understandable machine learning models.