SR4-Fit: A Unified Interpretable Rule-Based Machine Learning Framework for Informative and Trustworthy Decision-Making

arXiv:2609.34019 · cs.LG, cs.AI · Submitted 2026-09-27 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-27

Updated: 2026-09-27

Comments: 77 pages, 63 figures, 23 tables

Project page: https://christophm.github.io/interpretable-ml-book

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 86× in this paper · explained in 1 episode
• Interpretability — 61× in this paper · explained in 15 episodes
• Benchmark — 24× in this paper · explained in 2 episodes
• F1 Score — 15× in this paper · explained in 3 episodes
• Rule Understandability Score — 12× in this paper
• Robustness — 8× in this paper · explained in 5 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Sparsity — 7× in this paper · explained in 4 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Pruning — 5× in this paper · explained in 4 episodes
• Accountability — 3× in this paper · explained in 2 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• International Conference on Machine Learning and Applications — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Defense Advanced Research Projects Agency — 1× in this paper
• Explainability — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Institute for Community and Society Transformation — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Random Forest and Support Vector Machine — 1× in this paper
• Root Mean Squared Error — 1× in this paper

Sources

• A hybrid machine learning framework for analyzing human decision making through learning preferences

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