Model Agnostic Supervised Local Explanations

Understand

Model interpretability is an increasingly important component of practical machine learning.

• Some of the most common forms of interpretability systems are example-based, local, and global explanations.
• One of the main challenges in interpretability is designing explanation systems that can capture aspects of each 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 uses local linear modeling techniques along with a dual interpretation of random forests (both as a supervised neighborhood approach and as a feature selection method).