  Classification by neural nets and by tree-based methods are powerful tools of
machine learning. There exist interesting visualizations of the inner workings
of these and other classifiers. Here we pursue a different goal, which is to
visualize 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. This
is reflected in the (conditional and posterior) probability of the alternative
class (PAC). A high PAC indicates label bias, i.e. the possibility that the
case was mislabeled. The PAC is used to construct a silhouette plot which is
similar in spirit to the silhouette plot for cluster analysis (Rousseeuw,
1987). The average silhouette width can be used to compare different
classifications of the same dataset. We will also draw quasi residual plots of
the PAC versus a data feature, which may lead to more insight in the data. One
of these data features is how far each case lies from its given class. The
graphical displays are illustrated and interpreted on benchmark data sets
containing images, mixed features, and tweets.
