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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.