arXiv Machine Learning

Conditional Inference Trees and Forests for Feature Selection

arXiv:2607. 01417v1 Announce Type: new Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive.

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.

By Vadim Porvatov, Andrey Dukhovny, Andrey Lange

benchmarks

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.

benchmarks

arXiv Machine Learning

Sep 22

Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling

arXiv:2609.24126v1 Announce Type: cross Abstract: Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important fe...

By Xuhui Liu, Lili Zheng

benchmarks

arXiv Machine Learning

Sep 22

Beyond Point Prediction: Artificial Representative Trees with Uncertainty

arXiv:2609.24528v1 Announce Type: cross Abstract: Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs...

By Lea L. Mairh\"ofer, Silke Szymczak, Bj\"orn-Hergen Laabs, Tuwe L\"ofstr\"om-Cavallin

benchmarks safety

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.

By Youngjoon Park

benchmarks safety

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.

By Floris Jan Koster, Fabio Sigrist

arXiv Machine Learning

Aug 31

RiskBlend: A Multi-Signal Framework for Test Input Prioritization in Machine Learning Regression Testing

arXiv:2608.27704v1 Announce Type: new Abstract: When machine learning classifiers are retrained, inputs correctly classified by the previous model version may be misclassified by the updated version,...

By Madhusudan Srinivasan, Namith Nishal Raphae

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.

By Sabino Francesco Roselli, Eibe Frank

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arXiv AI

Sep 1

Adaptive Multi-Branching for Shallow Decision Tree Induction

arXiv:2608.29262v1 Announce Type: cross Abstract: Decision trees are attractive for tabular prediction tasks because each prediction follows an interpretable sequence of feature-threshold tests. Unde...

By Hanul Park, Jeonghoon Choi, Juseong Kim, Sanghun Sel, Giltae Song

efficiency benchmarks

arXiv AI

Jul 1

When to Truncate a Feature Ranking: A Residual-Overlap Stopping Rule for Subset Selection

arXiv:2606. 31686v1 Announce Type: cross Abstract: Feature rankings are widely used in supervised feature selection because they are simple, scalable and easy to interpret.

By Jesus S. Aguilar-Ruiz

Hugging Face Trending Papers

Sep 21

Beyond Point Prediction: Artificial Representative Trees with Uncertainty

Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs) were developed as interpretable surrogate models...

benchmarks safety

arXiv Machine Learning

Jun 8

Adaptive Conditional Forest Sampling for Spectral Risk Optimisation under Decision-Dependent Uncertainty

arXiv:2603. 12507v2 Announce Type: replace Abstract: Minimising a spectral risk objective, defined as a weighted combination of expected cost and Conditional Value-at-Risk (CVaR), is challenging when the uncertainty distribution is decision-dependent, making both surrogate modelling and simulation-based ranking sensitive to tail estimation error.

By Marcell T. Kurbucz

rag benchmarks

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