Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion
(2021)
Journal Article
When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is... Read More about Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion.