Comparing Model-agnostic Feature Selection Methods through Relative Efficiency

Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest.

Wrapper methods are commonly used because they are typically model-agnostic.

In this paper, we develop a general comparison framework for model-agnostic feature selection methods based on relative efficiency, using \emph{relative variability} $\sigma/\mu$ to account for different statistics having different means.

In particular we focus on state-of-the-art feature selection methods, the Generalized Covariance Measure (GCM) and Leave-One-Covariate-Out (LOCO) estimation.

In particular, we present a theoretical comparison under three model settings: linear models, non-linear additive models, and single index models that mimic a single-layer neural network.

We complement this with simulations and real data examples for the above models and mis-specified models.

Our theoretical results, along with empirical findings, demonstrate that GCM-related methods generally out-perform LOCO under suitable regularity conditions defined by a suitably defined correlation quantity which quantifies the asymptotic relative efficiency of these approaches.

Our simulations and real data analysis include widely used machine learning methods such as neural networks and gradient boosting trees.