Decision Tree Learning

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Decision tree learning studies how to divide data into a hierarchy of tests so that predictions can be made from clear decision paths. Common approaches include greedy CART-style splitting, globally optimized trees found with mixed-integer programming or search, and differentiable or oblique trees that learn more complex splits; Hoeffding trees adapt these ideas to streaming data. Current work also explores uncertainty, ensembles of near-optimal trees, and uses such as causal subgroup analysis, recourse, and policy design. A central tension is how to improve predictive accuracy and stability without making trees too large, costly to train, or difficult to explain.

Latest papers

69 papers5 papers in the last four weeks, down 55% on the four weeks before. 0.0% of all new papers.

TIDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors
Oct 21, 2026Jiayi Chen, Hui Li, Yujiong Shen +12LLM Evaluation Decision Tree Learning
Fudan University · Hunyuan Team, Tencent
Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees
Oct 13, 2026Abdoulaye Sakho, Emmanuel Malherbe, Erwan ScornetClass-Imbalanced Learning Instance Reweighting
LPSM
MotherTree: Meta-learning on synthetic data improves decision tree training
Oct 7, 2026Ziyuan Wang, Fredrik D. JohanssonDecision Tree Learning Meta-Learning
Chalmers University of Technology University of Gothenburg
From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification
Oct 7, 2026Yue Qiu, Zekang Du, Yiqun Diao +2Tabular Classification Decision Tree Learning
School of Computer Science and Technology, Huazhong University of Science and Technology · School of Computing, National University of Singapore
Largest Rashomon sets of decision trees for robust contextual optimization
Oct 3, 2026Lorenzo Bonasera, David PisingerDecision Tree Learning Distributionally Robust Optimization
DTU Management, Technical University of Denmark, Akademivej, Building 358, Kongens Lyngby, 2800, Denmark
Discovery of fully efficient fault indicators along a data-based diagnosis process
Sep 23, 2026Igor Bezmaternykh, Louise Travé-Massuyès, Elodie ChantheryDecision Tree Learning Fault Diagnosis
INSA Toulouse · LAAS-DISCO, Comue de Toulouse, ANITI · LAAS
An Exact Junction-Tree Extended Formulation for Optimal Classification Trees
Sep 21, 2026Jiancheng TU, WenqiFanDecision Tree Learning Classification
Department of Computing, The Hong Kong Polytechnic University · Department of Management and Marketing, The Hong Kong Polytechnic University
Beyond Point Prediction: Artificial Representative Trees with Uncertainty
Sep 21, 2026Lea L. Mairhöfer, Silke Szymczak, Björn-Hergen Laabs +1Interpretable ML Conformal Prediction
Institute of Medical Biometry and Statistics, University of Luebeck, Ratzeburger Allee 160 V24, 23562, Lübeck, Germany. · Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany. · CAIMed, Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine, Göttingen, Germany. +1
Explainable Predictive Condition-based Maintenance of Naval-Propulsion Systems using Fuzzy Logic
Sep 21, 2026Dionisis Kalogeropoulos, Georgia Sovatzidi, Panagiotis G. Kalozoumis +1Predictive Maintenance Decision Tree Learning
Department of Computer Science and Biomedical Informatics, University of Thessaly
Falling Trees: A Model Class for Interpretable Risk Prioritization
Sep 20, 2026Varun Babbar, Zachery Boner, Margo Seltzer +1Interpretable ML Decision Tree Learning
Department of Computer Science, Duke University, Durham, NC, USA · Department of Computer Science, University of British Columbia, Vancouver, BC, CA.
A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees
Sep 18, 2026Chenxuanyin Zou, Jiayang Ren, Qiangqiang Mao +3Decision Tree Learning Branch and Bound
Department of Chemical and Biological Engineering University of British Columbia · Department of Electrical and Computer Engineering University of British Columbia · Department of Computer Science University of British Columbia
Four Ways to Grow a Classifier and Why One of Them Cannot Learn
Sep 16, 2026Cagri TemelDecision Tree Learning
Hezarfen LLC Seattle, WA, USA
Learned Look-Ahead Splitting Rule for CART
Sep 14, 2026Andrew Gao, Tianlin Liu, Ruichen Han +1Decision Tree Learning Classification
Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA · Lynbrook High School, San Jose, CA 95129, USA · Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
Literati: Towards Anytime Optimal Shape Generalized Trees via AO*
Sep 8, 2026Nakul Upadhya, Eldan CohenTree Search Decision Tree Learning
Department of Mechanical and Industrial Engineering University of Toronto, Toronto, Canada
Scaling Optimal Classification Trees via Adaptive Feature and Sample Reduction
Sep 5, 2026Jiancheng Tu, Wenqi FanDecision Tree Learning Dynamic Programming
Department of Computing, The Hong Kong Polytechnic University · Department of Management and Marketing, The Hong Kong Polytechnic University
Learning Sparse Decision Trees via Transformer Variational Auto-Encoders
Sep 1, 2026Giacomo Fidone, Alessio Cascione, Riccardo GuidottiVariational Autoencoders Structured Sparsity
University of Pisa · ISTI-CNR
Adaptive Multi-Branching for Shallow Decision Tree Induction
Aug 29, 2026Hanul Park, Jeonghoon Choi, Juseong Kim +2Decision Tree Learning Tabular ML
Department of Information Convergence Engineering, Pusan National University, Busan, Rep. of Korea · School of Computer Science and Engineering, Pusan National University, Busan, Rep. of Korea
DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
Aug 20, 2026MD Saifur Rahman Mazumder, Feng YuDecision Tree Learning
Department of Mathematical Sciences University of Texas at El Paso
Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning
Aug 17, 2026Daniel Nowak Assis, Jean Paul Barddal, Fabrício EnembreckDecision Tree Learning Classification
Programa de P´os-Gradua¸c˜ao em Inform´atica (PPGIa), Pontif´ıcia Universidade Cat´olica do Paran´a (PUCPR), Rua Imaculada Concei¸c˜ao 1155, 80215-901, Curitiba, Brazil · Sorbonne Universit´e, CNRS, LIP6, Paris, France
On Stopping Rules and Spatial Adaptation for CART
Aug 16, 2026Zineng Xu, Yuchao Cai, Yan Shuo TanDecision Tree Learning Minimax Estimation
Department of Statistics and Data Science, National University of Singapore
GeoAI-based post-segmentation quality validation of building footprints via spatial feature engineering
Aug 10, 2026Shah Imran Ahsan Chowdhury, Kazi Jihadur Rashid, Rajsree Das Tuli +2Building Footprint Extraction Decision Tree Learning
Department of Computer Science and Engineering Jahangirnagar University Dhaka 1342, Bangladesh · Department of Geography Florida State University Tallahassee, FL 32306, USA · Department of Geography and Environmental Sustainability The University of Oklahoma Norman, OK 73019, USA +1
Backward Compatibility in Tree-Based Explanations and Enhanced CART Algorithm
Aug 9, 2026Hirofumi SuzukiExplanation Stability Decision Tree Learning
Fujitsu Limited Kawasaki, Kanagawa, Japan
Handling Missing Data in Probabilistic Regression Trees
Aug 6, 2026Taiane Schaedler Prass, Alisson Silva Neimaier, Guilherme PumiLearning with Missing Data Decision Tree Learning
a Graduate Program in Statistics - Universidade Federal do Rio Grande do Sul.
ArborEnum: Decision Tree Rashomon Sets over Continuous Features
Aug 5, 2026Zakk Heile, Hayden McTavish, Margo Seltzer +1Decision Tree Learning
Department of Computer Science, Duke University, Durham, USA · Department of Computer Science, University of British Columbia, Vancouver, Canada
Search Strategies for Optimal Classification and Regression Trees
Jul 30, 2026Jacobus G. M. van der Linden, Mim van den Bos, Emir DemirovićDecision Tree Learning Classification
Delft University of Technology
dtControl2+ $\varepsilon$ : Trading Optimality for Explainability in MDPs via Decision Trees
Jul 28, 2026Tereza Kinská, Jan Křetínský, Tobias Meggendorfer +2Markov Decision Processes Markov Models
Masaryk University, Brno, Czech Republic · Technical University of Munich, Munich, Germany · Lancaster University Leipzig, Leipzig, Germany +1
Mind the Missing Split: Resolving Feature Heterogeneity in Swarm Learning with Random Forests
Jul 28, 2026Mohammad Tajabadi, Dominik HeiderLearning with Missing Data Decentralized Learning
Institute of Medical Informatics, University of Münster, Münster, Germany
Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data
Jul 15, 2026Youssef Drissi, Markus Ettl, Shivaram Subramanian +2Decision Tree Learning Dynamic Pricing
IBM Research
Relevance-Aware Rule: Structural Deletion of Irrelevant Conditions in Decision Trees
Jul 15, 2026Jung-Sik Hong, Jeongeon Lee, Min Kyu Sim +1Decision Tree Learning
Department of Data Science, Seoul National University of Science and Technology
Algebraic Model Counting for Global Analysis of Optimal Decision Trees
Jul 2, 2026Hiroki ArimuraDecision Tree Learning
Hokkaido University, N14 W9, Kita-ku, Sapporo, Hokkaido, 060-0814 Japan

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TIDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors

Jiayi Chen, Hui Li, Yujiong Shen, Qiyuan Peng+11

Fudan University · Hunyuan Team, Tencent

LLM Evaluation Decision Tree Learning

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Benchmark performance is the dominant standard for comparing large language models (LLMs), typically summarized by a single aggregate score, such as accuracy, pass rate, or rubric score. This aggregate score provides limited information about a model's underlying capabilities, yet understanding these capabilities is crucial for model development and evaluation. To this end, we propose Tree-Induced Diagnostic Evaluation (TIDE), which recasts diagnostic evaluation as behavior-driven decision-tree induction to perform fine-grained capability diagnosis. TIDE recursively builds a hierarchical capability tree from benchmark samples and model scores: at each node, an LLM judge proposes candidate splits and performs sample-to-group classification, and behavior-based information gain selects among them. We apply TIDE to five models across four benchmarks to demonstrate its effectiveness, and find fine-grained capability contrasts invisible at the aggregate level: Claude Opus 4.6 achieves 100% on microeconomic market reasoning where GPT-5.4 scores 83.3%, but trails GPT-5.4 on contract law reasoning with 77.8% versus 88.9%. We hope TIDE can be extended to past and future benchmarks so that the rich behavioral signal in evaluation results is fully leveraged for understanding model capabilities.