arXiv Machine Learning
Jun 24
Experiments with Optimal Model Trees
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
Search Strategies for Optimal Classification and Regression Trees
arXiv:2607. 28170v1 Announce Type: new Abstract: Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging.
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Hugging Face Trending Papers
Jun 11
CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
Regression trees are among the most interpretable yet expressive model classes in machine learning. Historically, greedy induction has been the dominant approach for constructing well-performing regression trees.
arXiv Machine Learning
Jul 1
Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again
arXiv:2606. 30995v1 Announce Type: new Abstract: Recent work has shown that well-optimized individual decision trees can match complex black box models in some settings, primarily in noisy domains.
arXiv Machine Learning
Aug 7
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
arXiv:2409. 12788v3 Announce Type: replace Abstract: Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally optimize an impurity or information metric.
Hugging Face Trending Papers
Aug 20
DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
The paper introduces DICS, a clustering-based framework that uses data-informed priors to construct a compact set of candidate splits for decision tree classifiers. By incorporating class-aware structure, DICS reduces the split search space, preserving predictive performance while cutting training time. The authors provide theoretical analysis and experimental results showing comparable accuracy to exhaustive search across synthetic and benchmark datasets.
arXiv Machine Learning
Aug 21
DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
arXiv:2608. 20258v1 Announce Type: new Abstract: Decision tree-based models are widely used in machine learning due to their interpretability and strong empirical performance.
arXiv Machine Learning
Jul 17
Selecting Hyperparameters for Tree-Boosting
arXiv:2602. 05786v3 Announce Type: replace Abstract: Tree-boosting is a widely used machine learning technique for tabular data.
arXiv Machine Learning
Sep 10
Scaling Optimal Classification Trees via Adaptive Feature and Sample Reduction
arXiv:2609.05826v1 Announce Type: new Abstract: Dynamic programming for optimal classification trees becomes computationally expensive as the numbers of features and training samples increase. We dev...
arXiv Machine Learning
Sep 16
Learned Look-Ahead Splitting Rule for CART
The paper introduces a look‑ahead splitting rule for Classification and Regression Trees (CART) that evaluates candidate splits by the error reduction achieved after growing a conventional CART subtree beneath each split. To keep the method computationally feasible, a smart look‑ahead algorithm is proposed that learns downstream split values from node‑level features. Experiments on simulated data and two real datasets show that both full and smart look‑ahead methods outperform the standard greedy splitting strategy, especially in hierarchical or interaction‑driven scenarios.
arXiv Machine Learning
Aug 11
Backward Compatibility in Tree-Based Explanations and Enhanced CART Algorithm
arXiv:2608. 08674v1 Announce Type: new Abstract: In the operation of machine learning models, model update is a fundamental process that requires careful consideration of its impact on downstream decision-making.
arXiv Machine Learning
Aug 5
Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling
arXiv:2608. 03111v1 Announce Type: new Abstract: Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width.
arXiv Machine Learning
Sep 3
Simulating Classification Models for Ex-Ante Evaluation of Predict-Then-Optimize Methods
The paper extends ex‑ante evaluation of Predict‑Then‑Optimize methods from binary to multiclass classification by simulating predictions at specified performance levels and mapping prediction errors to decision regret. It introduces a first‑order approximation that estimates regret from individual misclassifications, reducing computational effort. Experiments show the simulation accurately reproduces target performance and that the approximation is close for some problems, though it falters when simultaneous misclassifications interact significantly.