mlsanityData to 7 Oct 2026

Feature Importance Measurement based on Decision Tree Sampling

arXiv 2307.13333 ↗First posted 25 Jul 2023cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Random forest is effective for prediction tasks but the randomness of tree generation hinders interpretability in feature importance analysis. To address this, we proposed DT-Sampler, a SAT-based method for measuring feature importance in tree-based model. Our method has fewer parameters than random forest and provides higher interpretability and stability for the analysis in real-world problems. An implementation of DT-Sampler is available at https://github.com/tsudalab/DT-sampler.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 39 references that are in the map. First posted on arXiv 25 Jul 2023.

Builds on

7 of 39 references.

References · 7

  1. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
  2. 2015Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction modelcited by 140
  3. 2017Learning Certifiably Optimal Rule Lists for Categorical Datacited by 50
  4. 2020In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Predictioncited by 42
  5. 2022FasterRisk: Fast and Accurate Interpretable Risk Scorescited by 21
  6. 2021Fast and More Powerful Selective Inference for Sparse High-order Interaction Modelcited by 15
  7. 2022A Confidence Machine for Sparse High-Order Interaction Modelcited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024An Information-Theoretic Approach to Analyze NLP Classification Taskscited by 1
  2. 2025TreeNet: Layered Decision Ensemblescited by 0