Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling
arXiv:2609.24126v1 Announce Type: new Abstract: Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important features, remains challenging. Existing model-agnostic methods primarily estimate feature importance or…
Read the full story at arXiv stat.ML ↗
Timeline · 1 report
- 2026-09-22 04:00 · arXiv stat.ML
Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling