Kernel of Partition Paths: A Unified Representation for Tree Ensembles

arXiv:2606.18853 · stat.ML, cs.LG · Submitted 2026-06-17 · Read on arXiv

Nicolas Mahler

stat.ML, cs.LG

Submitted: 2026-06-17

Comments: v3 (38 pages): adds a 40-dataset benchmark study with Friedman and Nemenyi analyses, leaf-only and node-weight ablations, and per-dataset supplementary tables; incorporates three correctness-focused revision passes. Results and conclusions unchanged

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

Terminology

• Trace — 20× in this paper · explained in 2 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Benchmark — 18× in this paper · explained in 2 episodes
• Robustness — 14× in this paper · explained in 6 episodes
• XGBoost — 13× in this paper · explained in 3 episodes
• Calibration — 9× in this paper · explained in 9 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Interpretability — 4× in this paper · explained in 15 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Feature Importance — 2× in this paper · explained in 2 episodes
• Shapley values — 2× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Axioms — 1× in this paper · explained in 2 episodes
• Causal Inference — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Expressivity — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Lasso — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Minimax Optimal Rates — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Unsupervised Learning — 1× in this paper · explained in 2 episodes

Sources

• AutoEncoder by Forest
• A survey and taxonomy of methods interpreting random forest models
• Local MDI+: Local Feature Importances for Tree-Based Models
• Learning Interpretable Characteristic Kernels via Decision Forests
• A Powerful Random Forest Featuring Linear Extensions (RaFFLE)
• Geometry- and Accuracy-Preserving Random Forest Proximities
• Autoencoding Random Forests

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