Nomograms for Visualizing Support Vector Machines Aleks Jakulin, Martin Mozina, Janez Demsar, Ivan Bratko, and Blaz Zupan   View Paper (PDF)   Return to Research Session 9 [Support Vector Machines] Abstract 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. ©2006 Association for Computing Machinery