ABSTRACT
In the previous chapter, we considered methods that draw a (high-dimensional) line through the feature space to separate classes. However, it’s clear that for some datasets, there simply will be no line that does a very good job. In practice, it doesn’t take much to get datasets where linear classication methods will not perform very well. However, once we accept that we aren’t going to be able to use a line (or hyperplane) in the feature space to divide the classes, there are a lot of possible curves we could choose. Furthermore, there are only a few probabilistic models that will yield useful nonlinear classication boundaries, and these tend to have a lot of parameters, so they are not easy to apply in practice.
