Barrera-Vicen A. , PALUZO HIDALGO, EDUARDO, Gutiérrez-Naranjo M.A.
No
CEUR Workshop Proc.
Conference Paper
Científica
0.191
01/01/2023
2-s2.0-85178587954
Local Interpretable Model-Agnostic Explanations (LIME) are a well-known approach to provide local interpretability to Machine Learning models. LIME uses an exponential smoothing kernel based on the kernel width value, which defines the width of the local neighbourhood. In this paper, we study the influence of the distances for these local explanations, and we explore the choice of kernel width to guarantee a fair performance comparison between the distances. © 2023 CEUR-WS. All rights reserved.
Explainability; Exponential smoothing; Interpretability; Kernel width; Local interpretable model-agnostic explanation; Local neighborhoods; Machine learning models; Performance comparison; Smoothing kernels; XAI; Lime