Visualization and Interpretation of SVM Classifiers

Many machine learning applications involve modeling sparse high dimensional data. Examples include genomics, brain imaging, time series prediction etc. A common problem in such studies is the understanding of complex data-analytic models, especially nonlinear highdimensional models such as Support Vector Machines (SVM). This paper provides a brief survey of the current techniques for the visualization and interpretation of SVM based classification models, and then highlights potential problems with such techniques. Finally, we present a simple graphical method for visualization and understanding of SVM models. Keywords— Interpretation of black-box models, model selection, support vector machines, histogram of projections.