document: Nomograms for visualizing support vector machines 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 .
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