Model Agnostic Supervised Local Explanations

Model interpretability is an increasingly important component of practical machine learning. Some ofthemost common forms ofinterpretability systems are example-based, local, and global explanations. One of the main challenges in interpretability isdesigning explanation systems thatcancapture aspects ofeach of these explanation types, in order to develop a more thorough understanding of the model. We address this challenge in a novel model called MAPLE that useslocallinearmodeling techniques alongwithadualinterpretation ofrandom forests (both as a supervised neighborhood approach and as a feature selection method).