Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling
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 conduct inference on it rather than directly selecting features, wher...