Comparison of feature importance measures as explanations for classification models

作者：Mirka Saarela, Susanne Jauhiainen · 发表于：SN Applied Sciences · 年份：2021 · DOI：10.1007/s42452-021-04148-9 · 被引用次数：463 · 研究领域：Explainable Artificial Intelligence (XAI)、Machine Learning in Healthcare、Artificial Intelligence in Healthcare

Abstract Explainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature importance. However, there are several different approaches how feature importances are being measured, most notably global and local. In this study we compare different feature importance measures using both linear (logistic regression with L1 penalization) and non-linear (random forest) methods and local interpretable model-agnostic explanations on top of them. These methods are applied to two datasets from the medical domain, the openly available breast cancer data from the UCI Archive and a recently collected running injury data. Our results show that the most important features differ depending on the technique. We argue that a combination of several explanation techniques could provide more reliable and trustworthy results. In particular, local explanations should be used in the most critical cases such as false negatives.