Figure 5.

SVM model for prognosis. The available data consist of basic clinical and demographic features; age and site of onset. The objective is to classify patients according to 3-year survival. In the input space (where features are interpretable), no linear hyperplane can divide the two patient populations. The SVM model projects the data into a higher dimensional space—in our example a three dimensional space. The set of two features is mapped to a set of three features. In the feature space, a linear hyperplane can be computed which discriminates the two populations accurately. The three features used for discrimination are unavailable for analysis and interpretability is lost in the process.