Learning Decision Trees as Amortized Structure Inference

• URL: http://arxiv.org/abs/2503.06985v1
• Date: Mon, 10 Mar 2025 07:05:07 GMT
• Title: Learning Decision Trees as Amortized Structure Inference
• Authors: Mohammed Mahfoud, Ghait Boukachab, Michał Koziarski, Alex Hernandez-Garcia, Stefan Bauer, Yoshua Bengio, Nikolay Malkin,
• Abstract summary: We propose a hybrid amortized structure inference approach to learn predictive decision tree ensembles given data.<n>We show that our approach, DT-GFN, outperforms state-of-the-art decision tree and deep learning methods on standard classification benchmarks.
• Score: 59.65621207449269
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Building predictive models for tabular data presents fundamental challenges, notably in scaling consistently, i.e., more resources translating to better performance, and generalizing systematically beyond the training data distribution. Designing decision tree models remains especially challenging given the intractably large search space, and most existing methods rely on greedy heuristics, while deep learning inductive biases expect a temporal or spatial structure not naturally present in tabular data. We propose a hybrid amortized structure inference approach to learn predictive decision tree ensembles given data, formulating decision tree construction as a sequential planning problem. We train a deep reinforcement learning (GFlowNet) policy to solve this problem, yielding a generative model that samples decision trees from the Bayesian posterior. We show that our approach, DT-GFN, outperforms state-of-the-art decision tree and deep learning methods on standard classification benchmarks derived from real-world data, robustness to distribution shifts, and anomaly detection, all while yielding interpretable models with shorter description lengths. Samples from the trained DT-GFN model can be ensembled to construct a random forest, and we further show that the performance of scales consistently in ensemble size, yielding ensembles of predictors that continue to generalize systematically.

Related papers

• Zero-Shot Decision Tree Construction via Large Language Models [2.005837558796176]
  We introduce an algorithm for constructing decision trees using large language models (LLMs) in a zero-shot manner based on Classification and Regression Trees (CART) principles.<n>Our approach leverages LLMs to perform operations essential for decision tree construction, including attribute discretization, probability calculation, and Gini index computation.
  arXiv  Detail & Related papers  (2025-01-27T17:48:48Z)
• Structural Entropy Guided Probabilistic Coding [52.01765333755793]
  We propose a novel structural entropy-guided probabilistic coding model, named SEPC. We incorporate the relationship between latent variables into the optimization by proposing a structural entropy regularization loss. Experimental results across 12 natural language understanding tasks, including both classification and regression tasks, demonstrate the superior performance of SEPC.
  arXiv  Detail & Related papers  (2024-12-12T00:37:53Z)
• A Neural Network Alternative to Tree-based Models [0.0]
  We show that our models, Sparse TABular NET or sTAB-Net with attention mechanisms, are more effective than tree-based models. They achieve better performance than post-hoc methods like SHAP.
  arXiv  Detail & Related papers  (2024-10-23T10:50:07Z)
• Dynamic Post-Hoc Neural Ensemblers [55.15643209328513]
  In this study, we explore employing neural networks as ensemble methods. Motivated by the risk of learning low-diversity ensembles, we propose regularizing the model by randomly dropping base model predictions. We demonstrate this approach lower bounds the diversity within the ensemble, reducing overfitting and improving generalization capabilities.
  arXiv  Detail & Related papers  (2024-10-06T15:25:39Z)
• Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later [76.66498833720411]
  We introduce a differentiable version of $K$-nearest neighbors (KNN) originally designed to learn a linear projection to capture semantic similarities between instances.<n>Surprisingly, our implementation of NCA using SGD and without dimensionality reduction already achieves decent performance on tabular data.<n>We conclude our paper by analyzing the factors behind these improvements, including loss functions, prediction strategies, and deep architectures.
  arXiv  Detail & Related papers  (2024-07-03T16:38:57Z)
• Learning accurate and interpretable decision trees [27.203303726977616]
  We develop approaches to design decision tree learning algorithms given repeated access to data from the same domain. We study the sample complexity of tuning prior parameters in Bayesian decision tree learning, and extend our results to decision tree regression. We also study the interpretability of the learned decision trees and introduce a data-driven approach for optimizing the explainability versus accuracy trade-off using decision trees.
  arXiv  Detail & Related papers  (2024-05-24T20:10:10Z)
• Order-based Structure Learning with Normalizing Flows [7.972479571606131]
  Estimating causal structure of observational data is a challenging search problem that scales super-exponentially with graph size. Existing methods use continuous relaxations to make this problem computationally tractable but often restrict the data-generating process to additive noise models (ANMs) We present Order-based Structure Learning with Normalizing Flows (OSLow), a framework that relaxes these assumptions using autoregressive normalizing flows.
  arXiv  Detail & Related papers  (2023-08-14T22:17:33Z)
• Hierarchical clustering with dot products recovers hidden tree structure [53.68551192799585]
  In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of the standard algorithm, in which clusters are merged by maximum average dot product and not, for example, by minimum distance or within-cluster variance. We demonstrate that the tree output by this algorithm provides a bona fide estimate of generative hierarchical structure in data, under a generic probabilistic graphical model.
  arXiv  Detail & Related papers  (2023-05-24T11:05:12Z)
• SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting [7.206754802573034]
  In this paper, we explore the close connections between TAR models and regression trees. We introduce a new forecasting-specific tree algorithm that trains global Pooled Regression (PR) models in the leaves. In our evaluation, the proposed tree and forest models are able to achieve significantly higher accuracy than a set of state-of-the-art tree-based algorithms.
  arXiv  Detail & Related papers  (2022-11-16T04:30:42Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Treeging [0.0]
  Treeging combines the flexible mean structure of regression trees with the covariance-based prediction strategy of kriging into the base learner of an ensemble prediction algorithm. We investigate the predictive accuracy of treeging across a thorough and widely varied battery of spatial and space-time simulation scenarios.
  arXiv  Detail & Related papers  (2021-10-03T17:48:18Z)
• Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption [127.439030701253]
  Probabilistic sentential decision diagrams are a class of structured-decomposable circuits. We propose a new scheme based on a partial closed-world assumption: data implicitly provide the logical base of the circuit. Preliminary experiments show that the proposed approach might properly fit training data, and generalize well to test data, provided that these remain consistent with the underlying logical base.
  arXiv  Detail & Related papers  (2021-07-26T12:01:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.