We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphically encode the complete model on a single page. The dimensionality of the visualization does not depend on the number of attributes, but merely on the properties of the kernel. To represent the effect of each predictive feature on the log odds ratio scale as required for the nomograms, we employ logistic regression to convert the distance from the separating hyperplane into a probability. Case studies on selected data sets show that for a technique thought to be a black-box, nomograms can clearly expose its internal structure. By providing an easy-to-interpret visualization the analysts can gain insight and study the effects of predictive factors.

Attribute interactions are the irreducible dependencies between attributes. Interactions underlie feature relevance and selection, the structure of joint probability and classification models: if and only if the attributes interact, they should be connected. While the issue of 2-way interactions, especially of those between an attribute and the label, has already been addressed, we introduce an operational definition of a generalized n-way interaction by highlighting two models: the reductionistic part-to-whole approximation, where the model of the whole is reconstructed from models of the parts, and the holistic reference model, where the whole is modelled directly. An interaction is deemed significant if these two models are significantly different. In this paper, we propose the Kirkwood superposition approximation for constructing part-to-whole approximations. To model data, we do not assume a particular structure of interactions, but instead construct the model by testing for the presence of interactions. The resulting map of significant interactions is a graphical model learned from the data. We confirm that the P-values computed with the assumption of the asymptotic X2 distribution closely match those obtained with the boot-strap.