Model-Agnostic Interpretation of Cancer Classification with Multi-Platform Genomic Data

Machine learning models are often criticised for being black-boxes. Recent work in this field has aimed to address this criticism by developing methods to explain the underlying behaviour of machine learning models. These explanations are designed to help the end-user interpret how the models input features are used to make a prediction. Here, we present an extension to one such method, referred to as local interpretable model-agnostic …