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.
Kernel of Partition Paths: A Unified Representation for Tree Ensembles
arXiv:2606. 18853v1 Announce Type: cross Abstract: A recent line of work has reframed individual decision trees as linear models on engineered features associated with their splits, opening routes for oracle inequalities and feature-importance reinterpretation, but leaving open the question of what unified geometric object a forest induces when one indexes its feature map by nodes rather than by splits.
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arXiv Machine Learning
Jun 30
A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest
arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.
arXiv Machine Learning
Jun 2
Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification
arXiv:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.
arXiv Machine Learning
Aug 6
Data-Aware and Scalable Sensitivity Analysis for Decision Tree Ensembles
arXiv:2602. 07453v2 Announce Type: replace Abstract: Decision tree ensembles are widely used in critical domains, making robustness and sensitivity analysis essential to their trustworthiness.
arXiv Machine Learning
1d ago
Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles
arXiv:2608. 15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable.
arXiv Machine Learning
Jun 15
A General Framework for Decision Trees via Bregman Divergences
arXiv:2606. 13984v1 Announce Type: cross Abstract: Decision trees are one of the fundamental tools in statistical learning due to their interpretability, flexibility, and their ability to adapt to nonlinear structures.
arXiv Machine Learning
Jul 28
Distributional Split Criteria for Random Forests: Extensions, Shrinkage, and the Robustness of Mean Splitting
arXiv:2607. 23721v1 Announce Type: cross Abstract: Distributional random forests replace mean-based CART splitting with criteria that compare the full conditional response distribution in candidate children.
arXiv Machine Learning
Jun 5
Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach
arXiv:2605. 13830v2 Announce Type: replace-cross Abstract: Decision tree ensembles (DTE) are a popular model for a wide range of AI classification tasks, used in multiple safety critical domains, and hence verifying properties on these models has been an active topic of study over the last decade.
Hugging Face Trending Papers
Jun 2
How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
Hyperparameter optimization (HPO) for Random Forest faces a specific difficulty in tuning the number of trees: the predictive score typically improves monotonically with ensemble size, so standard methods such as Tree-structured Parzen Estimator (TPE) and Hyperband require a predefined search range and often drive the estimate toward its right boundary. Early-stopping strategies avoid fixing such a range, but can be sensitive to score noise and prone to premature stopping.
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
Jun 3
How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
arXiv:2606. 03549v1 Announce Type: new Abstract: Hyperparameter optimization (HPO) for Random Forest faces a specific difficulty in tuning the number of trees: the predictive score typically improves monotonically with ensemble size, so standard methods such as Tree-structured Parzen Estimator (TPE) and Hyperband require a predefined search range and often drive the estimate toward its right boundary.
arXiv Machine Learning
Aug 5
LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
arXiv:2602. 22432v2 Announce Type: replace-cross Abstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty.