arXiv:2512. 11081v2 Announce Type: replace-cross Abstract: Feature and Interaction Importance (FII) methods are essential in supervised learning for assessing the relevance of input variables and their interactions in complex prediction models.
By Kata Vuk, Nicolas Alexander Ihlo, Merle Behr
arXiv:2608. 05880v1 Announce Type: cross Abstract: Interpreting clustering outcomes remains a fundamental challenge in data analysis, particularly in domains such as healthcare where meaningful patterns must be extracted from high-dimensional data.
By Benjamin Connor, Anna Jurek-Loughrey, Lu Bai, Muhammad Fahim
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.
By Robert Milletich, Justin Downes, Steve Goley, Newel Hirst
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:2105. 07610v5 Announce Type: replace-cross Abstract: Building trustworthy machine learning algorithms for biological applications requires adapting to data heterogeneity from different sources, batches, distributions, or studies.
By Maya Ramchandran, Rajarshi Mukherjee, Giovanni Parmigiani
arXiv:2512. 13003v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is essential for determining when a supervised model encounters inputs that differ meaningfully from its training distribution.
By Min Lu, Hemant Ishwaran