GLIME: General, Stable and Local LIME Explanation
Zeren Tan Yang Tian Jian Li
As black-box machine learning models grow in complexity and find applications
in high-stakes scenarios, it is imperative to provide explanations for their
predictions. Although Local Interpretable Model-agnostic Explanations (LIME)
[22] is a widely adpoted method for understanding model behaviors, it is
unstable with respect to random seeds [35,24,3] and exhibits low local fidelity
(i.e., how well the explanation approximates the model's local behaviors)
[21,16]. Our study shows that this instability problem stems from small sample
weights, leading to the dominance of regularization and slow convergence.
Additionally, LIME's sampling neighborhood is non-local and biased towards the
reference, resulting in poor local fidelity and sensitivity to reference
choice. To tackle these challenges, we introduce GLIME, an enhanced framework
extending LIME and unifying several prior methods. Within the GLIME framework,
we derive an equivalent formulation of LIME that achieves significantly faster
convergence and improved stability. By employing a local and unbiased sampling
distribution, GLIME generates explanations with higher local fidelity compared
to LIME. GLIME explanations are independent of reference choice. Moreover,
GLIME offers users the flexibility to choose a sampling distribution based on
their specific scenarios.