Combining many features in a classifier

You've spent this lesson engineering many features from the audio data - some contain information about how the audio changes in time, others contain information about the spectral content that is present.

The beauty of machine learning is that it can handle all of these features at the same time. If there is different information present in each feature, it should improve the classifier's ability to distinguish the types of audio. Note that this often requires more advanced techniques such as regularization, which we'll cover in the next chapter.

For the final exercise in the chapter, we've loaded many of the features that you calculated before. Combine all of them into an array that can be fed into the classifier, and see how it does.

Loop through each spectrogram, calculating the mean spectral bandwidth and centroid of each.

Take Hint (-15 XP)

Exercise

Combining many features in a classifier

Instructions 1/2