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
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:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.
By Youngjoon Park
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: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
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
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
arXiv:2511. 20851v3 Announce Type: replace-cross Abstract: Feature selection remains difficult in modern high-dimensional settings, and established methods such as Boruta and Recursive Feature Elimination are either computationally costly or lack a statistically justified stopping criterion for their importance scores.
By Mousam Sinha, Tirtha Sarathi Ghosh, Koushik Biswas, Ridam Pal
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.
By Silas Koemen
arXiv:2608. 04310v1 Announce Type: new Abstract: The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, feature importance, and customizability.
By Zakk Heile, Hayden McTavish, Margo Seltzer, Cynthia Rudin
arXiv:2606. 15237v1 Announce Type: cross Abstract: Ensemble classifiers are predictive models that combine the results of simpler base models, often by majority vote.
By Joseph Kalman, Amit Moscovich
arXiv:2607. 03999v1 Announce Type: cross Abstract: Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty.
By Pantelis Z. Hadjipantelis, Josephine Chiang, Karthik Nagesh