Systematic approach for explaining machine learning predictions

Abstract

A systematic explainer is described herein, which comprises local, model-agnostic, surrogate ML model-based explanation techniques that faithfully explain predictions from any machine learning classifier or regressor. The systematic explainer systematically generates local data samples around a given target data sample, which improves on exhaustive or random data sample generation algorithms. Specifically, using principles of locality and approximation of local decision boundaries, techniques described herein identify a hypersphere (or data sample neighborhood) over which to train the surrogate ML model such that the surrogate ML model produces valuable, high-quality information explaining data samples in the neighborhood of the target data sample. Combining this systematic local data sample generation and a supervised neighborhood selection approach to weighting generated data samples relative to the target data sample achieves high explanation fidelity, locality, and repeatability when generating explanations for specific predictions from a given model.