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Classification by neural nets and by tree-based methods are powerful tools ofmachine learning. There exist interesting visualizations of the inner workingsof these and other classifiers. Here we pursue a different goal, which is tovisualize the cases being classified, either in training data or in test data.An important aspect is whether a case has been classified to its given class(label) or whether the classifier wants to assign it to different class. Thisis reflected in the (conditional and posterior) probability of the alternativeclass (PAC). A high PAC indicates label bias, i.e. the possibility that thecase was mislabeled. The PAC is used to construct a silhouette plot which issimilar in spirit to the silhouette plot for cluster analysis (Rousseeuw,1987). The average silhouette width can be used to compare differentclassifications of the same dataset. We will also draw quasi residual plots ofthe PAC versus a data feature, which may lead to more insight in the data. Oneof these data features is how far each case lies from its given class. Thegraphical displays are illustrated and interpreted on benchmark data setscontaining images, mixed features, and tweets.

Silhouettes and quasi residual plots for neural nets and tree-based classifiers