In this chapter, we stepped out of our comfort zone when we built a music genre classifier. Not having a deep understanding of music theory, at first we failed to train a classifier that predicts the music genre of songs with reasonable accuracy using FFT. But then we created a classifier that showed really usable performance using MFC features.

In both cases, we used features that we understood only so much as to know how and where to put them into our classifier setup. The one failed, the other succeeded. The difference between them is that in the second case, we relied on features that were created by experts in the field.

And that is totally OK. If we are mainly interested in the result, we sometimes simply have to take shortcuts—we only have to make sure to take these shortcuts from experts in the specific domains. And because we had learned how to correctly measure the performance in this new multiclass classification problem, we took these shortcuts with confidence.

In the next chapter, we will look at how to apply techniques you have learned in the rest of the book to this specific type of data. We will learn how to use the mahotas computer vision package to preprocess images using traditional image processing functions.