Understanding Support Vector Machine Classifications: A Local Approach

Support vector machines are valuable for making classifications, but they lack the natural explanatory capability that many other classifiers possess. We suggest two methods for providing insight into support vector machine classifications. In the first, we report the support vectors most influential in the final classification for a particular test point. In the second, we determine which features of that test point would need to be altered (and by how much) in order to be placed on the separating surface between the two classifications. We also present a free-for-download software tool that enables users to visualize these insights graphically.