At the core of interpretable machine learning is the question of whether
humans are able to make accurate predictions about a model's behavior. Assumed
in this question are three properties of the interpretable output: coverage,
precision, and effort. Coverage refers to how often humans think they can
predict the model's behavior, precision to how accurate humans are in those
predictions, and effort is either the up-front effort required in interpreting
the model, or the effort required to make predictions about a model's behavior.
In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that
produces high-precision rule-based explanations for which the coverage
boundaries are very clear. We compare aLIME to linear LIME with simulated
experiments, and demonstrate the flexibility of aLIME with qualitative examples
from a variety of domains and tasks.