Observation-specific explanations through scattered data approximation
Valentina Ghidini Michael Multerer Jacopo Quizi Rohan Sen
This work introduces the definition of observation-specific explanations to
assign a score to each data point proportional to its importance in the
definition of the prediction process. Such explanations involve the
identification of the most influential observations for the black-box model of
interest. The proposed method involves estimating these explanations by
constructing a surrogate model through scattered data approximation utilizing
the orthogonal matching pursuit algorithm. The proposed approach is validated
on both simulated and real-world datasets.