  In multiclass classification of multidimensional data, the user wants to
build a model of the classes to predict the label of unseen data. The model is
trained on the data and tested on unseen data with known labels to evaluate its
quality. The results are visualized as a confusion matrix which shows how many
data labels have been predicted correctly or confused with other classes. The
multidimensional nature of the data prevents the direct visualization of the
classes so we design ClassSPLOM to give more perceptual insights about the
classification results. It uses the Scatterplot Matrix (SPLOM) metaphor to
visualize a Linear Discriminant Analysis projection of the data for each pair
of classes and a set of Receiving Operating Curves to evaluate their
trustworthiness. We illustrate ClassSPLOM on a use case in Arabic dialects
identification.
