Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data.

ABSTRACT: In recent years, methods were proposed for assigning feature importance scores to measure the contribution of individual features. While in some cases the goal is to understand a specific model, in many cases the goal is to understand the contribution of certain properties (features) to a real-world phenomenon. Thus, a distinction has been made between feature importance scores that explain a model and scores that explain the data. When explaining the data, machine learning models are used as proxies in settings where conducting many real-world experiments is expensive or prohibited. While existing feature importance scores show great success in explaining models, we demonstrate their limitations when explaining the data, especially in the presence of correlations between fea

SUBMITTER: Catav A 

PROVIDER: S-EPMC8460841 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature