# Fast Interpretable Greedy-Tree Sums

arXiv: https://arxiv.org/abs/2201.11931 · HTML: https://mlsanity.com/paper/2201.11931/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 90 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 22 of 22)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Stability](https://mlsanity.com/paper/1310.0150/) (2013)
- [Generalized and Scalable Optimal Sparse Decision Trees](https://mlsanity.com/paper/2006.08690/) (2020)
- [Veridical Data Science](https://mlsanity.com/paper/1901.08152/) (2019)
- [Explainable AI for Trees: From Local Explanations to Global Understanding](https://mlsanity.com/paper/1905.04610/) (2019)
- [PMLB v1.0: An open source dataset collection for benchmarking machine learning methods](https://mlsanity.com/paper/2012.00058/) (2020)
- [Learning Certifiably Optimal Rule Lists for Categorical Data](https://mlsanity.com/paper/1704.01701/) (2017)
- [Iterative Random Forests to detect predictive and stable high-order interactions](https://mlsanity.com/paper/1706.08457/) (2017)
- [Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead](https://mlsanity.com/paper/1811.10154/) (2018)
- [Adaptive wavelet distillation from neural networks through interpretations](https://mlsanity.com/paper/2107.09145/) (2021)
- [Unbiased Measurement of Feature Importance in Tree-Based Methods](https://mlsanity.com/paper/1903.05179/) (2019)
- [Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success](https://mlsanity.com/paper/1911.00190/) (2019)
- [The Implicit Regularization of Ordinary Least Squares Ensembles](https://mlsanity.com/paper/1910.04743/) (2019)
- [Local Linear Forests](https://mlsanity.com/paper/1807.11408/) (2018)
- [MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA](https://mlsanity.com/paper/2102.13347/) (2021)
- [A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds](https://mlsanity.com/paper/2110.09626/) (2021)
- [Estimation Stability with Cross Validation (ESCV)](https://mlsanity.com/paper/1303.3128/) (2013)
- [Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution](https://mlsanity.com/paper/1806.09762/) (2018)
- [Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees](https://mlsanity.com/paper/1905.07631/) (2019)

## Built on by (most cited first, 13 of 13)

- [ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs](https://mlsanity.com/paper/2505.17495/) (2025)
- [Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAP](https://mlsanity.com/paper/2505.11189/) (2025)
- [Local MDI+: Local Feature Importances for Tree-Based Models](https://mlsanity.com/paper/2506.08928/) (2025)
- [Adaptive Test-Time Intervention for Concept Bottleneck Models](https://mlsanity.com/paper/2503.06730/) (2025)
- [A Foundation Model for Zero-Shot Logical Rule Induction](https://mlsanity.com/paper/2605.04916/) (2026)
- [Efficient Constrained Graph Search for Post-hoc Error Correction in Binary Classifiers](https://mlsanity.com/paper/2401.04282/) (2024)
- [Theoretical and Empirical Advances in Forest Pruning](https://mlsanity.com/paper/2401.05535/) (2024)
- [Analyze Additive and Interaction Effects via Collaborative Trees](https://mlsanity.com/paper/2405.11477/) (2024)
- [RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data](https://mlsanity.com/paper/2504.06927/) (2025)
- [Evidential Rule Learning for Interpretable Classification with Abstention](https://mlsanity.com/paper/2608.05859/) (2026)
- [Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles](https://mlsanity.com/paper/2608.15725/) (2026)
- [Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugs](https://mlsanity.com/paper/2608.26209/) (2026)
- [Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data](https://mlsanity.com/paper/2610.14006/) (2026)

## Abstract (arXiv)

Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure. To overcome this bias, we propose Fast Interpretable Greedy-Tree Sums (FIGS), which generalizes the CART algorithm to simultaneously grow a flexible number of trees in summation. By combining logical rules with addition, FIGS is able to adapt to additive structure while remaining highly interpretable. Extensive experiments on real-world datasets show that FIGS achieves state-of-the-art prediction performance. To demonstrate the usefulness of FIGS in high-stakes domains, we adapt FIGS to learn clinical decision instruments (CDIs), which are tools for guiding clinical decision-making. Specifically, we introduce a variant of FIGS known as G-FIGS that accounts for the heterogeneity in medical data. G-FIGS derives CDIs that reflect domain knowledge and enjoy improved specificity (by up to 20% over CART) without sacrificing sensitivity or interpretability. To provide further insight into FIGS, we prove that FIGS learns components of additive models, a property we refer to as disentanglement. Further, we show (under oracle conditions) that unconstrained tree-sum models leverage disentanglement to generalize more efficiently than single decision tree models when fitted to additive regression functions. Finally, to avoid overfitting with an unconstrained number of splits, we develop Bagging-FIGS, an ensemble version of FIGS that borrows the variance reduction techniques of random forests. Bagging-FIGS enjoys competitive performance with random forests and XGBoost on real-world datasets.