Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding

ArXi:2504.20667v3 Announce Type: replace Post-hoc explainability is essential for understanding black-box machine learning models. Surrogate-based techniques are widely used for local and global model-agnostic explanations but have significant limitations. Local surrogates capture non-linearities but are computationally expensive and sensitive to parameters, while global surrogates are efficient but struggle with complex local behaviors.