So far, we have been lucky that every training data instance could easily be described by a vector of feature values. In the Iris dataset, for example, the flowers are represented by vectors containing values for the length and width of certain aspects of a flower. In the text-based examples, we could transform the text into bag-of-word representations and manually craft our own features that captured certain aspects of the texts.

It will be different in this chapter, when we try to classify songs by their genre. How would we, for instance, represent a three minute-long song? Should we take the individual bits of its MP3 representation? Probably not, since treating it like text and creating something like a bag of sound bites would certainly be way too complex. Somehow, we will have to convert a song into a vector of values that describe it sufficiently.