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In this work we present the novel ASTRID method for investigating whichattribute interactions classifiers exploit when making predictions. Attributeinteractions in classification tasks mean that two or more attributes togetherprovide stronger evidence for a particular class label. Knowledge of suchinteractions makes models more interpretable by revealing associations betweenattributes. This has applications, e.g., in pharmacovigilance to identifyinteractions between drugs or in bioinformatics to investigate associationsbetween single nucleotide polymorphisms. We also show how the found attributepartitioning is related to a factorisation of the data generating distributionand empirically demonstrate the utility of the proposed method.

Interpreting Classifiers through Attribute Interactions in Datasets