In this chapter, you learned that, sometimes, you can get rid of complete features using feature selection methods. We also saw that, in some cases, this is not enough, and we have to employ feature projection methods that reveal the real and the lower-dimensional structure in our data, hoping that the model has an easier time with it.
For sure, we only scratched the surface of the huge body of available dimensionality reduction methods. Still, we hope that we got you interested in this whole field, as there are lots of other methods waiting for you to pick them up. In the end, feature selection and projection are an art, just like choosing the right learning method or training model.
In Chapter 6, Clustering – Finding Related Posts, we will introduce clustering, which is an unsupervised learning technique. We will use it to find similar news posts for a given text...
